CN116228539A - Unmanned aerial vehicle remote sensing image stitching method - Google Patents
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Abstract
The invention relates to a method for splicing remote sensing images of an unmanned aerial vehicle, which comprises the following steps: acquiring an unmanned aerial vehicle remote sensing image, and preprocessing the unmanned aerial vehicle remote sensing image; extracting features of the preprocessed unmanned aerial vehicle remote sensing image to obtain optimal features; and carrying out image registration on the preprocessed remote sensing images of the unmanned aerial vehicle, fusing the matching results according to the optimal characteristics, and carrying out quality evaluation on the fused results. The invention provides a method for splicing remote sensing images of an unmanned aerial vehicle, which can accurately register the images, reduce splicing errors, realize seamless splicing and realize natural transition.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a method for splicing remote sensing images of an unmanned aerial vehicle.
Background
The unmanned aerial vehicle is used as an emerging aerial remote sensing platform, has the characteristics of high efficiency, flexibility, rapidness and low cost, and a digital camera and a digital video camera carried on the unmanned aerial vehicle can acquire high-resolution images. Unmanned aerial vehicle remote sensing is a novel remote sensing means, and application fields are wide, including agriculture, forestry, electric power, homeland resources, urban planning and the like, and because the unmanned aerial vehicle remote sensing platform is limited by flight height, camera focal length and the like in the aerial photographing process, the acquired images have the characteristics of low flight height and small image amplitude, the overall situation of a photographing area cannot be reflected, and the requirements and the application of the ground on information cannot be met, particularly in emergency disaster relief.
The existing unmanned aerial vehicle remote sensing image stitching process is to firstly conduct flight track planning, enable the unmanned aerial vehicle to acquire images along the planned flight track, and then stitch acquired image sequences. When the acquired image sequence does not meet the splicing requirement or does not completely cover the planning area, the unmanned aerial vehicle is utilized to acquire images again along the previously planned flight track, and then the images acquired twice are combined for splicing, so that the workload is large, the images are repeated in a large amount, and the splicing efficiency is low.
Disclosure of Invention
The invention aims to provide a method for splicing remote sensing images of an unmanned aerial vehicle, which can accurately register the images, reduce splicing errors, realize seamless splicing and realize natural transition.
In order to achieve the above object, the present invention provides the following solutions:
a method for unmanned aerial vehicle remote sensing image stitching, comprising:
acquiring an unmanned aerial vehicle remote sensing image, and preprocessing the unmanned aerial vehicle remote sensing image;
extracting features of the preprocessed unmanned aerial vehicle remote sensing image to obtain optimal features;
and carrying out image registration on the preprocessed remote sensing images of the unmanned aerial vehicle, fusing the matching results according to the optimal characteristics, and carrying out quality evaluation on the fused results.
Optionally, preprocessing the remote sensing image of the unmanned aerial vehicle includes:
and carrying out radiation correction on the unmanned aerial vehicle remote sensing image based on a histogram matching method, and carrying out geometric correction on the unmanned aerial vehicle remote sensing image subjected to radiation correction through image resampling.
Optionally, obtaining the optimal feature includes:
and carrying out Harris angular point detection on the preprocessed unmanned aerial vehicle remote sensing image to obtain image feature points, carrying out feature selection on the image feature points to obtain the optimal features, and eliminating uncorrelated or redundant features.
Optionally, performing image registration on the preprocessed remote sensing image of the unmanned aerial vehicle includes:
and calculating geographic coordinate projection information based on longitude and latitude and yaw angle information corresponding to the unmanned aerial vehicle remote sensing image, selecting the preprocessed unmanned aerial vehicle remote sensing image according to a stitching strategy, and carrying out image registration according to the geographic coordinate projection information to obtain a registration image, namely the matching result.
Optionally, fusing the matching result according to the optimal feature includes:
establishing a matching data set, constructing a similarity evaluation index based on the optimal characteristics, screening out matching points in the matching set, and calculating a transformation matrix based on the matching points;
and fusing the registration images by combining the transformation matrix through a weighted average fusion method, so as to realize the splicing of the unmanned remote sensing images.
Optionally, establishing the matching dataset includes: and establishing the one-to-many matching data set by adopting a query index KD tree mode.
Optionally, performing quality evaluation on the fusion result includes:
and carrying out quality evaluation on the fusion result based on the edge difference spectrum evaluation method, obtaining a quality evaluation result, presetting an evaluation threshold, calculating the position of an overlapping region of the fusion result if the quality evaluation result is smaller than the evaluation threshold, removing the position of the overlapping region of the registration image, fusing the registration image after the position of the overlapping region again, and if the quality evaluation result is larger than the evaluation threshold, not processing.
Optionally, before the quality evaluation of the fusion result, the method includes:
detecting whether the registration image after fusion is completed covers all planning areas, intercepting the blank area and partial remote sensing images nearby the blank area from the registration image after fusion is completed if the uncovered blank area exists, performing matching operation on the basis of the intercepted remote sensing images and the unmanned aerial vehicle remote sensing images, finding out the unmanned aerial vehicle remote sensing image with the highest matching rate, calculating the geographical coordinate projection information of the intercepted remote sensing images, acquiring the unmanned aerial vehicle remote sensing images again according to the geographical coordinate projection information of the intercepted remote sensing images, replacing the corresponding unmanned aerial vehicle remote sensing images with the acquired unmanned aerial vehicle remote sensing images again, and registering and fusing the images again.
The beneficial effects of the invention are as follows:
according to the invention, the images can be accurately registered by the unmanned aerial vehicle remote sensing image stitching method, the stitching error is reduced, seamless stitching is realized, the transition is natural, and the effective images are prevented from being repeatedly acquired and stitched by detecting and evaluating the stitching result, so that the workload is reduced, and the image stitching efficiency is improved.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a method for splicing remote sensing images of an unmanned aerial vehicle according to an embodiment of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In order that the above-recited objects, features and advantages of the present invention will become more readily apparent, a more particular description of the invention will be rendered by reference to the appended drawings and appended detailed description.
As shown in fig. 1, a method for splicing remote sensing images of an unmanned aerial vehicle includes: collecting unmanned aerial vehicle remote sensing images, and preprocessing the unmanned aerial vehicle remote sensing images; extracting features of the preprocessed unmanned aerial vehicle remote sensing image to obtain optimal features; and carrying out image registration on the preprocessed unmanned aerial vehicle remote sensing image, fusing the matching result according to the optimal characteristics, and carrying out quality evaluation on the fusion result.
Preprocessing the unmanned aerial vehicle remote sensing image comprises the following steps: carrying out radiation correction on the unmanned aerial vehicle remote sensing image based on a histogram matching method, carrying out geometric correction on the unmanned aerial vehicle remote sensing image after the radiation correction through image resampling, and specifically:
because unmanned aerial vehicle is small, light in weight, influenced by the air current greatly, stability and wind resistance are relatively poor, and the flight attitude slope, shake phenomenon are difficult to avoid, and these all can produce direct influence to the remote sensing image of acquireing, lead to the image to take place the distortion. The distorted image is geometrically corrected through image point coordinate transformation, image resampling and the like, so that the influence of geometric distortion on the image is eliminated, and the requirement of image registration is met.
The obtaining of the optimal characteristics comprises the following steps: performing Harris angular point detection on the preprocessed unmanned aerial vehicle remote sensing image to obtain image feature points, performing feature selection on the image feature points to obtain optimal features, and eliminating uncorrelated or redundant features, wherein the method specifically comprises the following steps: the Harris corner detection utilizes a moving window to calculate a gray level change value in an image, wherein the main flow comprises the steps of converting the gray level change value into a gray level image, calculating a difference image, gaussian smoothing, calculating a local extremum, confirming a corner, and further obtaining an image characteristic point.
The image registration of the preprocessed unmanned aerial vehicle remote sensing image comprises the following steps: calculating geographic coordinate projection information based on longitude and latitude and yaw angle information corresponding to the unmanned aerial vehicle remote sensing image, selecting the preprocessed unmanned aerial vehicle remote sensing image according to a stitching strategy, and performing image registration according to the geographic coordinate projection information to obtain a registration image, namely a matching result; calculating the geographic coordinate projection information includes: the remote sensing image coordinates and the geographic coordinates are converted by utilizing affine matrix parameters, wherein the affine matrix parameters comprise 6 parameters which are XE, xpixel, rgamma, YE, ypixel and Rgamma respectively, the relationship between the image row and column numbers and the geographic coordinates is described, XE and YE represent the geographic projection coordinates of the image pixels at the upper left corner of the image, xpixel, ypixel represent the ground resolution of the image pixels in the longitude and latitude directions respectively, and Rgamma represents the sine value of the image rotation angle.
Fusing the matching result according to the optimal characteristics comprises the following steps: establishing a matching data set, constructing a similarity evaluation index based on the optimal characteristics, screening out matching points in the matching set, and calculating a transformation matrix based on the matching points; the registration images are fused by a weighted average fusion method and a transformation matrix, so that the unmanned remote sensing images are spliced, specifically:
calculating the distance between the matching point pairs, uniformly dividing the maximum and minimum values of the distance values into 10 sections, wherein the frequency of each section is P= { P1, …, P10}, the frequency of the peak section is max (P), the corresponding section is the ith section, the matching point pairs in the sections [ i-1, i+1] are correct matching point pairs, and the matching point pair set is the found accurate matching point pair; and eliminating the error characteristic point pairs according to a random sampling consensus RANSAC algorithm so as to calculate a change matrix, wherein the idea of the weighted average fusion method is to firstly perform weighted calculation on pixel values of overlapped parts of two images and then perform superposition. Combining the transformation matrix to fuse the registration images, so as to realize the splicing of the unmanned remote sensing images;
the aim of image fusion is to combine registered images into a new image based on a certain transformation model, and splicing marks should not appear in an overlapping area, namely seamless splicing is needed, and transition is natural;
the method for screening the matching points adopts a query index KD tree mode, namely, n characteristic matching points in the image closest to each characteristic point in the image are selected through spatial distances to form a one-to-many hypothesis matching set, the spatial distances are adopted as evaluation indexes of the similarity of the characteristic points, and the spatial distances comprise weighted sums of Euclidean distances and pixel coordinate distances.
Establishing the matching dataset includes: by adopting a query index KD tree mode, a one-to-many matching data set is established.
The quality evaluation of the fusion result comprises the following steps: and carrying out quality evaluation on the fusion result based on an edge difference spectrum evaluation method, obtaining a quality evaluation result, presetting an evaluation threshold, calculating the position of an overlapping region of the fusion result if the quality evaluation result is smaller than the evaluation threshold, removing the position of the overlapping region of the registration image, fusing the registration image after the position of the overlapping region again, and if the quality evaluation result is larger than the evaluation threshold, not processing.
Before the fusion result is subjected to quality evaluation, the method comprises the following steps: detecting whether the fused registration image covers all planning areas, if so, intercepting the blank area and partial remote sensing images nearby the blank area from the fused registration image, performing matching operation on the basis of the intercepted remote sensing image and the remote sensing image of the unmanned aerial vehicle, finding out the remote sensing image of the unmanned aerial vehicle with the highest matching rate, calculating the geographic coordinate projection information of the intercepted remote sensing image, acquiring the remote sensing image of the unmanned aerial vehicle again according to the geographic coordinate projection information of the intercepted remote sensing image, replacing the corresponding remote sensing image of the unmanned aerial vehicle with the acquired remote sensing image of the unmanned aerial vehicle again, and registering and fusing the images again, wherein the method comprises the following steps of:
in the embodiment, the quality evaluation result is between 0 and 1, and the threshold value is 0.7; if the quality evaluation result is smaller than the threshold value, calculating the position of the overlapping region of the fusion result, removing the position of the overlapping region of the registration image, fusing the registration image after the position of the overlapping region again, and if the quality evaluation result is larger than the evaluation threshold value, not processing; before the quality evaluation is carried out on the fusion result, detecting whether the fused registration image covers all planning areas, if so, intercepting the blank area and partial remote sensing images nearby the blank area from the fused registration image, carrying out matching operation on the intercepted remote sensing images and the remote sensing images of the unmanned aerial vehicle, finding out the remote sensing image of the unmanned aerial vehicle with the highest matching rate, calculating the geographic coordinate projection information of the intercepted remote sensing image, acquiring the remote sensing image of the unmanned aerial vehicle again according to the geographic coordinate projection information of the intercepted remote sensing image, replacing the corresponding unmanned aerial vehicle data with the acquired remote sensing image of the unmanned aerial vehicle again, and carrying out image registration and fusion again until the spliced remote sensing image meeting the requirements is obtained.
The above embodiments are merely illustrative of the preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, but various modifications and improvements made by those skilled in the art to which the present invention pertains are made without departing from the spirit of the present invention, and all modifications and improvements fall within the scope of the present invention as defined in the appended claims.
Claims (8)
1. The unmanned aerial vehicle remote sensing image stitching method is characterized by comprising the following steps of:
acquiring an unmanned aerial vehicle remote sensing image, and preprocessing the unmanned aerial vehicle remote sensing image;
extracting features of the preprocessed unmanned aerial vehicle remote sensing image to obtain optimal features;
and carrying out image registration on the preprocessed remote sensing images of the unmanned aerial vehicle, fusing the matching results according to the optimal characteristics, and carrying out quality evaluation on the fused results.
2. The method of unmanned aerial vehicle remote sensing image stitching according to claim 1, wherein preprocessing the unmanned aerial vehicle remote sensing image comprises:
and carrying out radiation correction on the unmanned aerial vehicle remote sensing image based on a histogram matching method, and carrying out geometric correction on the unmanned aerial vehicle remote sensing image subjected to radiation correction through image resampling.
3. The method of unmanned aerial vehicle remote sensing image stitching according to claim 1, wherein obtaining the optimal features comprises:
and carrying out Harris angular point detection on the preprocessed unmanned aerial vehicle remote sensing image to obtain image feature points, carrying out feature selection on the image feature points to obtain the optimal features, and eliminating uncorrelated or redundant features.
4. The method of unmanned aerial vehicle remote sensing image stitching according to claim 1, wherein performing image registration on the preprocessed unmanned aerial vehicle remote sensing image comprises:
and calculating geographic coordinate projection information based on longitude and latitude and yaw angle information corresponding to the unmanned aerial vehicle remote sensing image, selecting the preprocessed unmanned aerial vehicle remote sensing image according to a stitching strategy, and carrying out image registration according to the geographic coordinate projection information to obtain a registration image, namely the matching result.
5. The method of unmanned aerial vehicle remote sensing image stitching according to claim 4, wherein fusing the matching results according to the optimal features comprises:
establishing a matching data set, constructing a similarity evaluation index based on the optimal characteristics, screening out matching points in the matching set, and calculating a transformation matrix based on the matching points;
and fusing the registration images by combining the transformation matrix through a weighted average fusion method, so as to realize the splicing of the unmanned remote sensing images.
6. The method of unmanned aerial vehicle remote sensing image stitching according to claim 5, wherein establishing the matching dataset comprises: and establishing the one-to-many matching data set by adopting a query index KD tree mode.
7. The method of unmanned aerial vehicle remote sensing image stitching according to claim 5, wherein performing a quality evaluation on the fusion result comprises:
and carrying out quality evaluation on the fusion result based on the edge difference spectrum evaluation method, obtaining a quality evaluation result, presetting an evaluation threshold, calculating the position of an overlapping region of the fusion result if the quality evaluation result is smaller than the evaluation threshold, removing the position of the overlapping region of the registration image, fusing the registration image after the position of the overlapping region again, and if the quality evaluation result is larger than the evaluation threshold, not processing.
8. The method for unmanned aerial vehicle remote sensing image stitching according to claim 4, wherein before the quality evaluation of the fusion result, the method comprises:
detecting whether the registration image after fusion is completed covers all planning areas, intercepting the blank area and partial remote sensing images nearby the blank area from the registration image after fusion is completed if the uncovered blank area exists, performing matching operation on the basis of the intercepted remote sensing images and the unmanned aerial vehicle remote sensing images, finding out the unmanned aerial vehicle remote sensing image with the highest matching rate, calculating the geographical coordinate projection information of the intercepted remote sensing images, acquiring the unmanned aerial vehicle remote sensing images again according to the geographical coordinate projection information of the intercepted remote sensing images, replacing the corresponding unmanned aerial vehicle remote sensing images with the acquired unmanned aerial vehicle remote sensing images again, and registering and fusing the images again.
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