CN113077468A - Quality detection method and device for radiation abnormality of hyperspectral satellite image - Google Patents
Quality detection method and device for radiation abnormality of hyperspectral satellite image Download PDFInfo
- Publication number
- CN113077468A CN113077468A CN202110638581.1A CN202110638581A CN113077468A CN 113077468 A CN113077468 A CN 113077468A CN 202110638581 A CN202110638581 A CN 202110638581A CN 113077468 A CN113077468 A CN 113077468A
- Authority
- CN
- China
- Prior art keywords
- image
- sub
- detection
- missing
- hyperspectral satellite
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10036—Multispectral image; Hyperspectral image
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
Landscapes
- Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Image Processing (AREA)
Abstract
The invention provides a method and a device for detecting the quality of radiation abnormality of a hyperspectral satellite image, which comprises the following steps: acquiring a hyperspectral satellite image to be detected, wherein the hyperspectral satellite image comprises a plurality of sub-images, and each sub-image corresponds to a spectrum segment; judging whether the sub-image is an image with a bad wave band; performing deletion detection on the sub-image in response to the sub-image not being an image with a bad waveband; in response to the sub-image having a missing image area, replacing the DN value of the missing image area with an invalid value; performing blind pixel detection and stripe detection on the sub-image after the missing detection to generate a first detection result; performing integral missing detection on the hyperspectral satellite image to generate a second detection result; and generating marking data of radiation abnormality of the hyperspectral satellite image according to the first detection result and the second detection result. The anomaly of each spectral band image of the hyperspectral satellite image can be effectively detected, and the workload of manual quality inspection is greatly reduced.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of satellite image processing technology, and more particularly, to a method and an apparatus for detecting quality of radiation anomaly of a hyperspectral satellite imagery.
Background
The hyperspectral satellite image is obtained by continuously remotely sensing and imaging the ground object by using a narrow and continuous spectral channel. The spectral resolution of the infrared spectrum from visible light to short wave is up to the order of nanometers (nm), and the infrared spectrum generally has the characteristic of more spectrum bands, the number of spectral channels is up to tens or even more than hundreds, and the spectral channels are usually continuous, so the hyperspectral remote sensing is also generally called imaging spectral remote sensing.
The technology of remote sensing with high spectral resolution obtains a plurality of very narrow spectrum continuous image data in the range of visible light, near infrared, intermediate infrared and thermal infrared spectral bands of electromagnetic spectrum. Its imaging spectrometer can collect hundreds of very narrow spectral band information.
Compared with the traditional remote sensing technology with low spectral resolution, the hyperspectral remote sensing provides wider application in earth observation and environmental investigation, greatly improves the resolution and identification capability of ground objects and greatly increases imaging channels.
However, in the prior art, a quick and effective method for detecting the quality of the hyperspectral satellite image is lacked, the conventional 4-spectrum and 8-spectrum multispectral quality detection process obviously cannot adapt to the hyperspectral satellite images of hundreds of spectra frequently, and the conventional 4-spectrum or 8-spectrum multispectral quality detection process is adopted, so that the workload of manual quality detection is greatly increased, the quality detection efficiency is reduced, and the effectiveness and the data performance of hyperspectral satellite image data information are influenced.
Disclosure of Invention
According to the embodiment of the disclosure, the quality detection scheme for the radiation abnormity of the hyperspectral satellite image is provided, the workload and the quality detection efficiency of manual quality detection can be reduced, and the validity and the data property of the hyperspectral satellite image data information are improved.
In a first aspect of the present disclosure, a method for detecting quality of radiation anomaly of a hyperspectral satellite image is provided, including:
acquiring a hyperspectral satellite image to be detected, wherein the hyperspectral satellite image comprises a plurality of sub-images, and each sub-image corresponds to a spectrum segment;
judging whether the sub-image is an image with a bad wave band; performing deletion detection on the sub-image in response to the sub-image not being an image with a bad waveband; in response to the sub-image having a missing image area, replacing the DN value of the missing image area with an invalid value;
performing blind pixel detection and stripe detection on the sub-image after the missing detection to generate a first detection result;
carrying out integral missing detection on the hyperspectral satellite image to generate a second detection result;
and generating marking data of radiation abnormity of the hyperspectral satellite image according to the first detection result and the second detection result.
The foregoing aspect and any possible implementation manner further provide an implementation manner, where the determining whether the sub-picture is a picture with a bad band includes:
and for one of the sub-images, judging whether DN values of the sub-images are all 0, if the DN values of the sub-images are all 0, the sub-image is the image with the bad wave band.
The above aspect and any possible implementation manner further provide an implementation manner, where performing the missing detection on the sub-image in response to that the sub-image is not an image with a bad waveband includes:
responding that the sub-image is not an image with a bad wave band, carrying out binarization on the sub-image according to a preset threshold value to generate a binary image, carrying out corrosion and expansion on the binary image, removing bad lines and stripes, and determining a rectangular area after corrosion and expansion as an invalid image spot.
The above-described aspect and any possible implementation manner further provide an implementation manner, where in response to a missing picture area of the sub-picture, replacing a DN value of the missing picture area with an invalid value includes:
and in response to the sub-image having the missing image area, replacing the DN value of the missing image area with a 0 value.
As to the foregoing aspect and any possible implementation manner, there is further provided an implementation manner, where performing blind pixel detection and streak detection on the sub-image after the missing detection to generate a first detection result, including:
calculating the mean value of each row of the sub-images to obtain a mean value sequence, and determining the row of which the absolute value of the deviation from the mean value sequence is greater than a preset multiple of the standard deviation of the mean value sequence as a blind pixel row;
comparing all pixels of adjacent columns of the sub-image, and determining the current column as a stripe column in response to the fact that the proportion of pixels with the pixel values of the current column larger or smaller than the pixel value of the next column is larger than a preset threshold value;
and generating a first detection result according to the blind pixel detection result and the stripe detection result.
The above-described aspects and any possible implementations further provide an implementation, and the method further includes:
judging whether the pixels of the stripe row are positioned at the position of the edge joint with the image or not for the stripe row;
and marking the stripe row as an inter-slice splicing stripe row in response to the pixel of the stripe row being located at the position bordering on the image.
As for the above-mentioned aspect and any possible implementation manner, an implementation manner is further provided, where the performing overall missing detection on the hyperspectral satellite image to generate a second detection result includes:
and comparing the missing image areas in each sub-image, judging whether the missing image areas in each sub-image overlap or not, responding to the overlapping of the missing image areas in different overlapped sub-images, and determining the overlapped image areas as the missing areas of the hyperspectral satellite image.
In a second aspect of the present disclosure, there is provided a quality detection apparatus for radiation abnormality of hyperspectral satellite imagery, including:
the hyperspectral satellite image acquisition module is used for acquiring a hyperspectral satellite image to be detected, wherein the hyperspectral satellite image comprises a plurality of sub-images, and each sub-image corresponds to a spectrum segment;
the sub-image detection module is used for judging whether the sub-image is an image with a bad wave band; performing deletion detection on the sub-image in response to the sub-image not being an image with a bad waveband; in response to the sub-image having a missing image area, replacing the DN value of the missing image area with an invalid value; performing blind pixel detection and stripe detection on the sub-image after the missing detection to generate a first detection result;
the integral missing detection module is used for carrying out integral missing detection on the hyperspectral satellite image to generate a second detection result;
and the detection data generation module is used for generating the marking data of the radiation abnormity of the hyperspectral satellite image according to the first detection result and the second detection result.
In a third aspect of the present disclosure, an electronic device is provided, comprising a memory having stored thereon a computer program and a processor implementing the method as described above when executing the program.
In a fourth aspect of the present disclosure, a computer-readable storage medium is provided, on which a computer program is stored, which program, when being executed by a processor, is adapted to carry out the method as set forth above.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of the embodiments of the present disclosure, nor are they intended to limit the scope of the present disclosure. Other features of the present disclosure will become apparent from the following description.
According to the quality detection method for radiation abnormity of the hyperspectral satellite image, the abnormity of each spectrum segment image of the hyperspectral satellite image can be effectively detected, the workload of manual quality inspection is greatly reduced, the quality inspection efficiency is improved, objective and effective hyperspectral satellite image data information is provided, and the data performance is improved.
Drawings
The above and other features, advantages and aspects of various embodiments of the present disclosure will become more apparent by referring to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, like or similar reference characters designate like or similar elements, and wherein:
fig. 1 is a flowchart illustrating a method for detecting radiation anomaly quality of a hyperspectral satellite image according to a first embodiment of the disclosure;
fig. 2 is a flowchart illustrating a quality detection method for radiation anomaly of a hyperspectral satellite image according to a second embodiment of the disclosure;
fig. 3 is a functional structure diagram of a quality detection apparatus for radiation anomaly of a hyperspectral satellite image according to a third embodiment of the disclosure;
fig. 4 shows a schematic structural diagram of a quality detection apparatus for radiation anomaly of a hyperspectral satellite image according to a fourth embodiment of the disclosure.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described clearly and completely with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are some, but not all embodiments of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
In addition, the term "and/or" herein is only one kind of association relationship describing an associated object, and means that there may be three kinds of relationships, for example, a and/or B, which may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The quality detection method for the radiation abnormity of the hyperspectral satellite image can be used for detecting the quality of the radiation abnormity of the multiband satellite image. Specifically, as an embodiment of the present disclosure, as shown in fig. 1, a flowchart of a method for detecting radiation anomaly quality of a hyperspectral satellite image according to a first embodiment of the present disclosure is shown. The quality detection method for radiation abnormality of the hyperspectral satellite image according to the embodiment can comprise the following steps:
s101: the hyperspectral satellite image to be detected is obtained, the hyperspectral satellite image comprises a plurality of sub-images, and each sub-image corresponds to a spectrum segment.
The method is equivalent to the conventional 4-spectrum and 8-spectrum satellite images, the hyperspectral satellite images have a plurality of spectrum segments, and the method cannot be applied to the quality detection process of the 4-spectrum and 8-spectrum satellite images in the quality detection process of the hyperspectral satellite images. Therefore, the embodiment of the disclosure provides a quality detection method for radiation abnormality of a hyperspectral satellite image. Firstly, a hyperspectral satellite image to be detected needs to be acquired, wherein the hyperspectral satellite image comprises a plurality of sub-images, and each sub-image corresponds to a spectral band.
S102: judging whether the sub-image is an image with a bad wave band; performing deletion detection on the sub-image in response to the sub-image not being an image with a bad waveband; and in response to the sub-image having the missing image area, replacing the DN value of the missing image area with an invalid value.
In this embodiment, in the process of performing quality detection on radiation abnormality of a hyperspectral satellite image, quality detection in the same flow is performed on each of a plurality of sub-images, and in the process of performing quality detection, processing may be performed in parallel or may be performed in a certain order. In the quality detection process of a sub-image, first, it is determined whether the sub-image is an image with a bad band. Specifically, whether DN values (pixel brightness values) of the sub-images are all 0 is judged, if the DN values of the sub-images are all 0, the sub-images are the images with bad wave bands, at the moment, the images with the bad wave bands are marked as the images with the bad wave bands, and the subsequent quality detection of the spectrum bands corresponding to the sub-images is skipped.
And when the sub-image is not the image with the bad wave band, performing deletion detection on the sub-image. Specifically, since the short-wave infrared hyperspectral sensor of the hyperspectral satellite image is composed of 4 pieces of CCDs (charge coupled devices), when the satellite detector is started or closed, the conditions of inconsistent angles, inconsistent startup/shutdown time and the like may occur, so that a part of pixel blocks of the image is an invalid value. This phenomenon occurs in all spectral bands, and therefore, for a sub-image, first, binarization is performed according to a threshold value, and the sub-image is converted into a binary image, that is, the sub-image is converted into an image with pixel values of only 0 and 1. For example, a pixel having a pixel value of 128 or more may be assigned 1, and a pixel having a pixel value of 128 or less may be assigned 0, so that the gray image may be converted into a binary image. And after the sub-image is converted into a binary image, carrying out corrosion and expansion to remove the influence of bad lines and stripes, and finally extracting an invalid image spot. Specifically, for a binary image, an operator is defined, which may be a polygonal line or an irregular figure, and the pixel value in the operator is only 0 and 1, and the operator is moved in the binary image according to a predetermined order, and the pixel value of the operator overlapped with the binary image is maximized (i.e., eroded), for example, the pixel value of a pixel in the binary image is 0, and the pixel value of the operator overlapped with the pixel is 1, and the pixel value of the pixel in the binary image is set to 1 (the maximum value of 0 and 1), and the process of dilation is similar to the process of erosion, but the pixel value of the operator overlapped with the binary image is minimized. After the binary image is subjected to erosion and expansion processing, a rectangular region in the processed binary image is determined as an invalid image patch, and all pixel values in the invalid image patch are replaced by 0 or 255.
S103: and performing blind pixel detection and stripe detection on the sub-image after the missing detection to generate a first detection result.
In the hyperspectral satellite image, blind pixels are generally longitudinal, and a certain row or several rows of pixel values of the image are extremely high or extremely low, so that the phenomenon appears as black or white stripes in the image. Meanwhile, the streak noise is also generally longitudinal, and is represented by obvious radiation difference between a certain row or several rows of picture elements of the image and other picture elements beside the certain row or several rows of picture elements.
Therefore, it is necessary to further perform blind pixel detection and streak detection on the sub-image after the missing detection, and generate a first detection result.
S104: and carrying out integral missing detection on the hyperspectral satellite image to generate a second detection result.
After each sub-image of the hyperspectral satellite image is detected and a first detection result is generated, the hyperspectral satellite image can be subjected to deletion detection on the whole. In this embodiment, whether the hyperspectral satellite image is entirely missing is determined according to the area proportion of the invalid pattern spots in all the sub-images after the missing detection in the sub-images. For example, an average value of the area ratios of the invalid pattern spots in the sub-images may be obtained, and the average value is compared with a preset threshold value, so as to determine whether the hyperspectral satellite image is missing as a whole, and a second detection result is generated according to the whole missing condition of the hyperspectral satellite image.
S105: and generating marking data of radiation abnormity of the hyperspectral satellite image according to the first detection result and the second detection result.
In this embodiment, after a second detection result that the hyperspectral satellite image is entirely missing is generated, the first detection result and the second detection result may be integrated to generate the labeled data of the hyperspectral satellite image with radiation abnormality.
According to the quality detection method for radiation abnormity of the hyperspectral satellite image, the abnormity of each spectrum segment image of the hyperspectral satellite image can be effectively detected, the workload of manual quality inspection is greatly reduced, the quality inspection efficiency is improved, objective and effective hyperspectral satellite image data information is provided, and the data performance is improved.
Fig. 2 is a flowchart of a quality detection method for radiation anomaly of a hyperspectral satellite image according to a second embodiment of the disclosure. The method of the embodiment may include the following steps:
s201: the hyperspectral satellite image to be detected is obtained, the hyperspectral satellite image comprises a plurality of sub-images, and each sub-image corresponds to a spectrum segment.
S202: judging whether the sub-image is an image with a bad wave band; performing deletion detection on the sub-image in response to the sub-image not being an image with a bad waveband; and in response to the sub-image having the missing image area, replacing the DN value of the missing image area with an invalid value.
S203: performing blind pixel detection and stripe detection on the sub-image after the missing detection, and judging whether pixels of a stripe row are positioned at the position of the edge joint with the image or not for the stripe row; and marking the stripe row as an inter-chip splicing stripe row to generate a first detection result in response to the position of the pixel of the stripe row, which is connected with the image.
The quality detection method is mainly used for detecting the quality of the image acquired by the short-wave infrared camera. Because the short wave infrared camera is formed by splicing 4 CCDs, the phenomenon of inconsistent radiation may occur at the spliced part. Therefore, after the sub-image is subjected to the stripe detection, it is necessary to further determine whether the stripe row belongs to the inter-slice splicing stripe.
Specifically, when the sub-image after the deletion detection is subjected to blind pixel detection, the pixel values of each row of the sub-image may be averaged to obtain a mean sequence, and a row whose absolute value of deviation from the mean sequence is greater than a preset multiple of a standard deviation of the mean sequence is determined as a blind pixel row. The following formula can be used for blind pixel detection:
in the formula (I), the compound is shown in the specification,
as a mean value for each column of the current spectral segment,
for the number of columns of the current spectral band,μis the average of all the columns of the current spectrum,
and judging a threshold value for the blind pixel.
When the stripe detection is performed on the sub-image after the missing detection, all pixels in adjacent columns of the sub-image can be compared, and in response to the fact that the proportion of pixels with the pixel values of the current column larger or smaller than the pixel value of the next column is larger than a preset threshold value, the current column is determined as a stripe column. The following formula may be used for streak detection:
in the formula
In order to satisfy the number of pixels of the condition,
for the number of lines in the current spectral band,
and
the threshold value is judged for the stripe,
and
representing two adjacent columns of picture elements.
The mean of the adjacent columns is then judged, and if the change is a certain percentage, the column is marked as a stripe:
after the blind pixel detection and the stripe detection of the sub-image are finished, judging whether the pixel of the stripe row is positioned at the position of the edge joint with the image or not for the stripe row; and marking the stripe row as an inter-slice splicing stripe row in response to the position of the pixel of the stripe row, which is in contact with the image, and generating a first detection result, namely marking the blind pixel, the stripe and the inter-slice splicing defect of the sub-image in the data of the sub-image.
S204: and carrying out integral missing detection on the hyperspectral satellite image to generate a second detection result.
S205: and generating marking data of radiation abnormity of the hyperspectral satellite image according to the first detection result and the second detection result.
Steps S201, S202, S204, and S205 in this embodiment are similar to those in the first embodiment, and are not repeated in this embodiment.
According to the quality detection method for radiation abnormity of the hyperspectral satellite image, the abnormity of each spectrum segment image of the hyperspectral satellite image can be effectively detected, the workload of manual quality inspection is greatly reduced, the quality inspection efficiency is improved, objective and effective hyperspectral satellite image data information is provided, and the data performance is improved.
It is noted that while for simplicity of explanation, the foregoing method embodiments have been described as a series of acts or combination of acts, it will be appreciated by those skilled in the art that the present disclosure is not limited by the order of acts, as some steps may, in accordance with the present disclosure, occur in other orders and concurrently. Further, those skilled in the art should also appreciate that the embodiments described in the specification are exemplary embodiments and that acts and modules referred to are not necessarily required by the disclosure.
The above is a description of embodiments of the method, and the embodiments of the apparatus are further described below.
Fig. 3 is a functional structure diagram of a quality detection apparatus for radiation anomaly of a hyperspectral satellite image according to a third embodiment of the disclosure. The quality detection device for radiation abnormality of hyperspectral satellite images of the embodiment comprises:
the hyperspectral satellite image acquisition module 301 is configured to acquire a hyperspectral satellite image to be detected, where the hyperspectral satellite image includes a plurality of sub-images, and each sub-image corresponds to a spectrum segment.
A sub-image detection module 302, configured to determine whether the sub-image is an image with a bad band; performing deletion detection on the sub-image in response to the sub-image not being an image with a bad waveband; in response to the sub-image having a missing image area, replacing the DN value of the missing image area with an invalid value; and performing blind pixel detection, stripe detection and inter-slice splicing detection on the sub-images after the missing detection to generate a first detection result.
And the overall missing detection module 303 is configured to perform overall missing detection on the hyperspectral satellite image to generate a second detection result.
A detection data generating module 304, configured to generate label data of radiation anomaly of the hyperspectral satellite image according to the first detection result and the second detection result.
It can be clearly understood by those skilled in the art that, for convenience and brevity of description, the specific working process of the described module may refer to the corresponding process in the foregoing method embodiment, and is not described herein again.
Fig. 4 is a schematic structural diagram illustrating an apparatus for determining an axle counting section in a logical section path according to a fourth embodiment of the present disclosure. The terminal device shown in fig. 4 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
As shown in fig. 4, the computer system includes a Central Processing Unit (CPU) 401 that can perform various appropriate actions and processes based on a program stored in a Read Only Memory (ROM) 402 or a program loaded from a storage section 408 into a Random Access Memory (RAM) 403. In the RAM403, various programs and data necessary for system operation are also stored. The CPU 401, ROM 402, and RAM403 are connected to each other via a bus 404. An input/output (I/O) interface 405 is also connected to bus 404.
The following components are connected to the I/O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including a display device such as a Cathode Ray Tube (CRT), a Liquid Crystal Display (LCD), and the like, and a speaker; a storage section 408 including a hard disk and the like; and a communication section 409 including a network interface card such as a LAN card, a modem, or the like. The communication section 409 performs communication processing via a network such as the internet. Drivers 410 are also connected to the I/O interface 405 on an as needed basis. A removable medium 411 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 410 on an as-needed basis, so that a computer program read out therefrom is mounted on the storage section 408 on an as-needed basis.
In particular, based on the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 409, and/or installed from the removable medium 411. The computer program performs the above-described functions defined in the method of the present application when executed by a Central Processing Unit (CPU) 401.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a load programmable logic device (CPLD), and the like.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program codes, when executed by the processor or controller, cause the functions/operations specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
Further, while operations are depicted in a particular order, this should be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
Claims (10)
1. The quality detection method for radiation abnormality of the hyperspectral satellite image is characterized by comprising the following steps:
acquiring a hyperspectral satellite image to be detected, wherein the hyperspectral satellite image comprises a plurality of sub-images, and each sub-image corresponds to a spectrum segment;
judging whether the sub-image is an image with a bad wave band; performing deletion detection on the sub-image in response to the sub-image not being an image with a bad waveband; in response to the sub-image having a missing image area, replacing the DN value of the missing image area with an invalid value;
performing blind pixel detection and stripe detection on the sub-image after the missing detection to generate a first detection result;
carrying out integral missing detection on the hyperspectral satellite image to generate a second detection result;
and generating marking data of radiation abnormity of the hyperspectral satellite image according to the first detection result and the second detection result.
2. The method for detecting the quality of the radiation abnormality of the hyperspectral satellite image according to claim 1, wherein the determining whether the sub-image is an image with a bad waveband comprises:
and for one of the sub-images, judging whether DN values of the sub-images are all 0, if the DN values of the sub-images are all 0, the sub-image is the image with the bad wave band.
3. The method for detecting the quality of the radiation anomaly of the hyperspectral satellite image according to claim 2, wherein the performing the missing detection on the sub-image in response to the sub-image not being an image with a bad waveband comprises:
responding that the sub-image is not an image with a bad wave band, carrying out binarization on the sub-image according to a preset threshold value to generate a binary image, carrying out corrosion and expansion on the binary image, removing bad lines and stripes, and determining a rectangular area after corrosion and expansion as an invalid image spot.
4. The method for detecting the quality of the radiation anomaly of the hyperspectral satellite image according to claim 3, wherein the step of replacing the DN value of the missing image area with an invalid value in response to the sub-image having the missing image area comprises the steps of:
and in response to the sub-image having the missing image area, replacing the DN value of the missing image area with a 0 value.
5. The method for detecting the quality of the radiation anomaly of the hyperspectral satellite image according to claim 4, wherein the performing the blind pixel detection and the streak detection on the sub-image after the missing detection to generate a first detection result comprises:
calculating the mean value of each row of the sub-images to obtain a mean value sequence, and determining the row of which the absolute value of the deviation from the mean value sequence is greater than a preset multiple of the standard deviation of the mean value sequence as a blind pixel row;
comparing all pixels of adjacent columns of the sub-image, and determining the current column as a stripe column in response to the fact that the proportion of pixels with the pixel values of the current column larger or smaller than the pixel value of the next column is larger than a preset threshold value;
and generating a first detection result according to the blind pixel detection result and the stripe detection result.
6. The method for detecting the quality of the radiation anomaly of the hyperspectral satellite image according to claim 5, further comprising:
judging whether the pixels of the stripe row are positioned at the position of the edge joint with the image or not for the stripe row;
and marking the stripe row as an inter-slice splicing stripe row in response to the pixel of the stripe row being located at the position bordering on the image.
7. The method for detecting the quality of the radiation anomaly of the hyperspectral satellite image according to claim 6, wherein the detecting the integral absence of the hyperspectral satellite image to generate a second detection result comprises:
and comparing the missing image areas in each sub-image, judging whether the missing image areas in each sub-image overlap or not, responding to the overlapping of the missing image areas in different overlapped sub-images, and determining the overlapped image areas as the missing areas of the hyperspectral satellite image.
8. A quality detection device for radiation abnormality of hyperspectral satellite images is characterized by comprising:
the hyperspectral satellite image acquisition module is used for acquiring a hyperspectral satellite image to be detected, wherein the hyperspectral satellite image comprises a plurality of sub-images, and each sub-image corresponds to a spectrum segment;
the sub-image detection module is used for judging whether the sub-image is an image with a bad wave band; performing deletion detection on the sub-image in response to the sub-image not being an image with a bad waveband; in response to the sub-image having a missing image area, replacing the DN value of the missing image area with an invalid value; performing blind pixel detection and stripe detection on the sub-image after the missing detection to generate a first detection result;
the integral missing detection module is used for carrying out integral missing detection on the hyperspectral satellite image to generate a second detection result;
and the detection data generation module is used for generating the marking data of the radiation abnormity of the hyperspectral satellite image according to the first detection result and the second detection result.
9. An electronic device comprising a memory and a processor, the memory having stored thereon a computer program, wherein the processor, when executing the program, implements the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored, which program, when being executed by a processor, carries out the method according to any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110638581.1A CN113077468B (en) | 2021-06-08 | 2021-06-08 | Quality detection method and device for radiation abnormality of hyperspectral satellite image |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110638581.1A CN113077468B (en) | 2021-06-08 | 2021-06-08 | Quality detection method and device for radiation abnormality of hyperspectral satellite image |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113077468A true CN113077468A (en) | 2021-07-06 |
| CN113077468B CN113077468B (en) | 2021-08-31 |
Family
ID=76617125
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110638581.1A Active CN113077468B (en) | 2021-06-08 | 2021-06-08 | Quality detection method and device for radiation abnormality of hyperspectral satellite image |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113077468B (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115953322A (en) * | 2023-01-15 | 2023-04-11 | 山东产研卫星信息技术产业研究院有限公司 | Stain removing method for satellite remote sensing image |
| CN116778232A (en) * | 2023-06-01 | 2023-09-19 | 自然资源部国土卫星遥感应用中心 | SAR image vertical stripe detection method, device, equipment and storage medium |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101916440A (en) * | 2010-08-09 | 2010-12-15 | 哈尔滨工程大学 | Hyperspectral abnormity detection method based on digital image morphology theory |
| CN104330164A (en) * | 2014-08-05 | 2015-02-04 | 凯迈（洛阳）测控有限公司 | Infrared focal plane array blind pixel detection method and device |
| CN105678777A (en) * | 2016-01-12 | 2016-06-15 | 武汉大学 | Feature-combined optical satellite image cloud and cloud shadow detection method |
| CN111353968A (en) * | 2018-12-20 | 2020-06-30 | 中国科学院沈阳自动化研究所 | Infrared image quality evaluation method based on blind pixel detection and analysis |
| US20200218930A1 (en) * | 2015-12-07 | 2020-07-09 | The Climate Corporation | Cloud detection on remote sensing imagery |
| CN111666363A (en) * | 2020-04-22 | 2020-09-15 | 国家信息中心 | Method and device for slicing and quality control of mass high-resolution image data |
| CN111861934A (en) * | 2020-07-29 | 2020-10-30 | 贵阳欧比特宇航科技有限公司 | Hyperspectral satellite image data production, mosaic and metadata manufacturing method |
| CN112529807A (en) * | 2020-12-15 | 2021-03-19 | 北京道达天际科技有限公司 | Relative radiation correction method and device for satellite image |
-
2021
- 2021-06-08 CN CN202110638581.1A patent/CN113077468B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101916440A (en) * | 2010-08-09 | 2010-12-15 | 哈尔滨工程大学 | Hyperspectral abnormity detection method based on digital image morphology theory |
| CN104330164A (en) * | 2014-08-05 | 2015-02-04 | 凯迈（洛阳）测控有限公司 | Infrared focal plane array blind pixel detection method and device |
| US20200218930A1 (en) * | 2015-12-07 | 2020-07-09 | The Climate Corporation | Cloud detection on remote sensing imagery |
| CN105678777A (en) * | 2016-01-12 | 2016-06-15 | 武汉大学 | Feature-combined optical satellite image cloud and cloud shadow detection method |
| CN111353968A (en) * | 2018-12-20 | 2020-06-30 | 中国科学院沈阳自动化研究所 | Infrared image quality evaluation method based on blind pixel detection and analysis |
| CN111666363A (en) * | 2020-04-22 | 2020-09-15 | 国家信息中心 | Method and device for slicing and quality control of mass high-resolution image data |
| CN111861934A (en) * | 2020-07-29 | 2020-10-30 | 贵阳欧比特宇航科技有限公司 | Hyperspectral satellite image data production, mosaic and metadata manufacturing method |
| CN112529807A (en) * | 2020-12-15 | 2021-03-19 | 北京道达天际科技有限公司 | Relative radiation correction method and device for satellite image |
Non-Patent Citations (2)
| Title |
|---|
| 张长兴 等: "热红外高光谱成像仪光谱匹配盲元检测算法", 《红外与激光工程》 * |
| 闫小明 等: "卫星遥感影像信息缺失检测", 《测绘与空间地理信息》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115953322A (en) * | 2023-01-15 | 2023-04-11 | 山东产研卫星信息技术产业研究院有限公司 | Stain removing method for satellite remote sensing image |
| CN115953322B (en) * | 2023-01-15 | 2023-07-14 | 山东产研卫星信息技术产业研究院有限公司 | Stain removing method for satellite remote sensing image |
| CN116778232A (en) * | 2023-06-01 | 2023-09-19 | 自然资源部国土卫星遥感应用中心 | SAR image vertical stripe detection method, device, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113077468B (en) | 2021-08-31 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113077468B (en) | Quality detection method and device for radiation abnormality of hyperspectral satellite image | |
| US11120565B2 (en) | Image registration method, image registration device and storage medium | |
| Ma et al. | Shadow segmentation and compensation in high resolution satellite images | |
| US8879865B2 (en) | Panchromatic sharpening method of spectral image based on fusion of overall structural information and spatial detail information | |
| CN111307727B (en) | Water body water color abnormity identification method and device based on time sequence remote sensing image | |
| CN111260616A (en) | Insulator crack detection method based on Canny operator two-dimensional threshold segmentation optimization | |
| JP3914570B2 (en) | MURA detection apparatus and method | |
| CN111598913B (en) | Image segmentation method and system based on robot vision | |
| US20060067569A1 (en) | Image inspection device, image inspection method, and image inspection program | |
| CN109712084B (en) | Image restoration method, image restoration system and flat panel detector | |
| CN112529807B (en) | Relative radiation correction method and device for satellite image | |
| CN115205223A (en) | Visual detection method and device for transparent object, computer equipment and medium | |
| CN113785181A (en) | OLED screen point defect judgment method and device, storage medium and electronic equipment | |
| CN111598889A (en) | Grading ring inclination fault identification method and device and computer equipment | |
| KR20190023374A (en) | Method for testing display pannel | |
| Montanez et al. | Photovoltaic module segmentation and thermal analysis tool from thermal images | |
| CN112101337A (en) | Ultraviolet spectrum identification method and system for external insulation discharge type of high-voltage electrical equipment and storage medium | |
| CN115690089A (en) | Image enhancement preprocessing method and system for weak defect detection | |
| CN110008949B (en) | Image target detection method, system, device and storage medium | |
| CN117456371B (en) | Group string hot spot detection method, device, equipment and medium | |
| CN114529500A (en) | Defect inspection method for display substrate | |
| US11574397B2 (en) | Image processing device, image processing method, and computer readable recording medium | |
| CN116958905A (en) | Power transmission line fault monitoring method, system, medium and equipment | |
| CN117274154A (en) | Porcelain insulator string defect identification method and system based on discharge ultraviolet video | |
| CN105260993A (en) | Hyperspectral image stripe missing restoring method based on edge constraint and self-adaptive morphological filter |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |