CN103456011A - Improved hyperspectral RX abnormal detection method by utilization of complementary information - Google Patents
Improved hyperspectral RX abnormal detection method by utilization of complementary information Download PDFInfo
- Publication number
- CN103456011A CN103456011A CN2013103933415A CN201310393341A CN103456011A CN 103456011 A CN103456011 A CN 103456011A CN 2013103933415 A CN2013103933415 A CN 2013103933415A CN 201310393341 A CN201310393341 A CN 201310393341A CN 103456011 A CN103456011 A CN 103456011A
- Authority
- CN
- China
- Prior art keywords
- data
- infrared
- image
- wave
- target
- 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.)
- Pending
Links
Images
Landscapes
- Image Processing (AREA)
- Closed-Circuit Television Systems (AREA)
Abstract
The invention relates to an improved hyperspectral RX abnormal detection method by the utilization of complementary information. A classic RX operator is respectively used for performing abnormal detection on visible near-infrared data and short wave infrared data in the same scene, and target judgment of the initial abnormal detection is obtained. On the basis, sensors are used for obtaining redundancy and complementarity of the information. Combined with a decision fusion method based on a rule, a final judged result of the RX abnormal detection is obtained. Experimental simulation is performed on actually-measured hyperspectral data, and the effectiveness of the algorithm is verified. The algorithm can effectively utilize redundancy and the complementary information of the two sets of data, and therefore the target detection rate is finally improved.
Description
Technical field
The invention belongs to a kind of high spectrum RX of modified method for detecting abnormality that utilizes complementary information, belong to hyperspectral data processing method and applied technical field, be applicable to the high-spectral data object detection field.
Background technology
Target in hyperspectral remotely sensed image organically combines traditional spatial image dimension information and the spectrum dimension information of reflection atural object radiation characteristic together, has very high spectral resolution, for the meticulous target detection of atural object provides good condition.The Hyperspectral imaging target detection technique is accompanied by the development of imaging spectral technology and rises, and in the remote sensing image application, has very important significance.Usually algorithm of target detection need to be used scene priori to a certain degree, comprises the priori image informations such as expertise, the curve of spectrum.Yet, in practice, these prioris are difficult to obtain.And still lack at present effective spectra inversion algorithm, curve of spectrum storehouse also is left to be desired, therefore, without the Outlier Detection Algorithm of priori, in the remote sensing image application, have very important significance.
Outlier Detection Algorithm is considered as the singular point under certain distribution occasion by the little target in high spectrum image, and the impact point that makes to convert in rear data by particular procedure is outstanding, and then automatically detects abnormal object.Wherein the most representative method is exactly the RX method that Reed and Yu propose.Its hypothesis local background obedience multivariate normal distribution, describe background model by the covariance matrix of estimating local background.Similar algorithm also has the UTD algorithm, and this algorithm replaces with even vector to the matched signal in the RX algorithm by measurement signal.The people such as Chang have proposed a lot of improvement algorithms on the basis of classical RX algorithm, such as methods such as adopting correlation matrix, describe background model, and then the detection operator be improved, and they mainly include CRXD, NRXD etc.In practical application, directly use RX to detect operator, while carrying out the high resolution image Anomaly target detection, often detect poor effect, can produce a lot of false-alarms zone.The non-singularity of not enough for the number of samples of estimate covariance matrix in local background, sample and the factors such as " pollutions " that is vulnerable to abnormal data all can have a strong impact on the estimation of background covariance matrix, make the covariance matrix of estimation can not the accurate description background model, thereby cause the detection hydraulic performance decline of RX algorithm.
Summary of the invention
The present invention is directed to the deficiencies in the prior art, propose a kind of RX Hyperspectral imaging method for detecting abnormality based on decision level fusion.
The inventive method comprises the following steps:
1) pre-service of high-spectral data.
Each wave band of spectrum picture is realized showing with gray-scale map, spectrum picture to each wave band carries out visual examination, determine that the effect due to Atmospheric Absorption, refraction and Scattering Factors produces the abnormal wave band of considerable influence and due to other factors, spectroscopic data produced the abnormal wave band of larger noise spectroscopic data, described abnormal wave band refers to imaging to show the atural object characteristic wave bands, abnormal wave band is done to direct rejecting and process.The pretreated method of data is:
data
1=pre_processing(data
1),data
2=pre_processing(data
2)
2) registration of image.
At first visible near-infrared and short-wave infrared image are carried out to image registration and image filtering.Then the short-wave infrared image after registration and visible near-infrared image are carried out respectively to the processing of RX abnormality detection, obtain a preliminary decision-making, obtain the bianry image that two width detect.Select the fusion standard of identical false alarm rate as preliminary decision-making, use the RX method for detecting abnormality:
out
1=RX(data
1,XJ),out
2=RX(data
2,XJ)
The preliminary target decision result obtained.
Be below the RX method for detecting abnormality:
Be the high resolution image data that N, image dimension are B for the pixel number, suppose that the observation pixel means the spectrum vector x that a dimension is B, wherein x=[x
1, x
2..., x
b], corresponding binary object detection hypothesized model is as follows:
H
0:x(n)=x
0(n) (1)
H
1:x(n)=x
0(n)+t
Wherein, x
0(n) represent non-target background pixel, t represents goal pels, H
0be expressed as the hypothesis of background clutter, H
1mean the hypothesis that contains target.
On two-value statistical model basis, adopt the generalized likelihood-ratio test method to deduce and show that RX detects operator, its expression formula is:
Adopt the reduced form of RX operator:
δ
RXD(x)=(x-μ)
TC
-1(x-μ)
In formula: the decision threshold that η is operator; The estimated value that μ is the background mean value vector; The estimated value of the covariance matrix that C is the background sample.
3) the decision level rule merges.
According to the Expert opinion zone, divide target area and background area, carry out rule and merge preparation.Target area is designated as to area
1, background area is designated as area
2; Then carry out the "or" logic in target area, in background area, carry out " with " logic." from " and "or" concrete operations and different remote sensing instrument between complementation and redundancy closely related, be that the arithmetical logic of reflection multi-sensor information fusion is described.Total target is when removing partial redundance information, give prominence to and embody the message complementary sense that the two sensors foreign matter detects.Carrying out rule fusion concrete grammar at fusion center is:
Obtain fusion results.
Above-mentioned various in, data
1, data
2be respectively the visible near-infrared data of 75 wave band and 80 wave band short-wave infrared data; Pre_processing is the pre-processing image data function; XJ means false alarm rate, as the bridge that connects two groups of preliminary rulings; out
1, out
2be respectively the preliminary target decision result after the RX abnormality detection of visible near-infrared, short-wave infrared data;
with
mean respectively the bianry image after the visible near-infrared and short-wave infrared RX in target area detects,
with
mean respectively the bianry image after the visible near-infrared and short-wave infrared RX in background area detects, fusion (x, y) is the fusion center court verdict.
The present invention is directed to the data characteristics of high-spectrum remote-sensing, at first experimental data is carried out to pre-service, the image after registration is carried out respectively to the RX abnormality detection, obtain preliminary decision-making, finally at fusion center, carry out decision level fusion, obtain final testing result.Parameter in the present invention arranges simply, has obtained and has detected preferably effect, has made up the limitation of local background's covariance matrix in traditional RX algorithm.
The accompanying drawing explanation
Fig. 1 is decision-making and fusion figure;
Fig. 2 is the inventive method process flow diagram;
The visible near-infrared gray-scale map that Fig. 3 (a) is the 30th wave band;
The short-wave infrared gray-scale map that Fig. 3 (b) is the 30th wave band;
Fig. 4 (a) is visible near-infrared Data Detection figure as a result;
Fig. 4 (b) is short-wave infrared Data Detection figure as a result;
Fig. 4 (c) is Pixel-level fusion detection figure as a result;
Fig. 4 (d) is decision level rule fusion detection figure as a result.
Embodiment
Below in conjunction with accompanying drawing, the invention will be further described.
What the present embodiment adopted is one group of actual measurement high-spectral data, after contextual data is carried out to image registration, Band fusion, these group data are 226 Χ 502 pixels, there are two kinds of describing modes, respectively the visible and near infrared spectrum data of 80 wave bands, the short-wave infrared data of 75 wave bands, Fig. 3 (a) is the visible near-infrared gray-scale map of the 30th wave band, Fig. 3 (b) is the short-wave infrared gray-scale map of the 30th wave band.
Experiment scene has comprised take 1 van, 2 semitrailers of a large amount of vegetation (willow woods and harvested milpa) under overall background.Include 10 two class targets (being applied as respectively green material A, material B) that scribble the camouflage paint vehicle in scene, they are placed respectively up and down.
By the inventive method process flow diagram, as Fig. 2, can find out clearly design procedure of the present invention.
At first, the good visible near-infrared and short-wave infrared data to registration, utilize the airspace filter method to due to the effect of Atmospheric Absorption, refraction and Scattering Factors, spectroscopic data being produced to the abnormal wave band of considerable influence in data and abnormal wave band that other factors produce larger noise to spectroscopic data carries out filtering, obtain the data data after airspace filter
1and data
2.Again, the image after airspace filter is carried out to the RX abnormality detection, using identical false alarm rate as the judgment condition that concerns of decision level fusion, the result data after the RX abnormality detection is out
1and out
2.For ease of the superiority of comparison algorithm, experiment is all to be controlled under an identical false alarm rate (0.47%), and the foreign matter target of then selecting thresholding to complete Various types of data detects automatically.Testing result is as Fig. 4 (a) and Fig. 4 (b).For outstanding decision level fusion effect is very good, added one group of testing result that Pixel-level merges in experiment, as Fig. 4 (c), finally RX testing result figure is carried out to the decision level rule and merge, the decision level fusion process flow diagram, as Fig. 1.Obtain fusion results, as Fig. 4 (d).Table 1 is the experimental data statistics table of comparisons.
The table 1 experimental result statistics table of comparisons
From the statistics table of comparisons 1, can find out through rule-based decision level fusion, under identical false alarm rate (0.47%), 53.59% of the 15.93% and 75 wave band short-wave infrareds that the target detection rate is visible near-infrared from 80 wave bands are promoted to and merge 86.91%, and simple Pixel-level merges the complementary information that can not effectively utilize two groups of data (visible near-infrared with short-wave infrared data) under certain condition.In experiment, the verification and measurement ratio that Pixel-level merges is than the verification and measurement ratio of 75 wave band short-wave infrareds also low (53.06% pair 53.59%).This is mainly that this can affect the estimation of background covariance matrix, can not bring into play the usefulness that Pixel-level merges because there are some abnormal datas (because the band imaging limitation of infrared sensor causes) in the short-wave infrared data.Compare that Pixel-level merges and 80 wave bands are visible near-infrared, the testing result of 75 wave band short-wave infrareds, the algorithm that the present invention proposes can effectively utilize redundancy and the complementary information of two groups of data.By mutually revising by short-wave infrared and visible near-infrared data, utilized message complementary sense, finally improved the target detection rate.In addition, also illustrated that the decision level visual fusion has higher serious forgiveness, and there is stronger antijamming capability.
Claims (1)
1. the high spectrum RX of a modified method for detecting abnormality that utilizes complementary information is characterized in that the method comprises the following steps:
1) pre-service of high-spectral data;
Each wave band of spectrum picture is realized showing with gray-scale map, spectrum picture to each wave band carries out visual examination, determine that the effect due to Atmospheric Absorption, refraction and Scattering Factors produces the abnormal wave band of considerable influence and due to other factors, spectroscopic data produced the abnormal wave band of larger noise spectroscopic data, described abnormal wave band refers to imaging to show the atural object characteristic wave bands, abnormal wave band is done to direct rejecting and process; The pretreated method of data is:
2) registration of image;
At first visible near-infrared and short-wave infrared image are carried out to image registration and image filtering; Then the short-wave infrared image after registration and visible near-infrared image are carried out respectively to the processing of RX abnormality detection, obtain a preliminary decision-making, obtain the bianry image that two width detect; Select the fusion standard of identical false alarm rate as preliminary decision-making, use the RX method for detecting abnormality:
The preliminary target decision result obtained;
Be below the RX method for detecting abnormality:
For the pixel number, be
, the image dimension is
high resolution image data, suppose observation pixel mean that a dimension is
the spectrum vector
, wherein
, corresponding binary object detection hypothesized model is as follows:
Wherein,
represent non-target background pixel,
represent goal pels,
be expressed as the hypothesis of background clutter,
mean the hypothesis that contains target;
On two-value statistical model basis, adopt the generalized likelihood-ratio test method to deduce and show that RX detects operator, its expression formula is:
Adopt the reduced form of RX operator:
(4)
In formula:
decision threshold for operator;
estimated value for the background mean value vector;
estimated value for the covariance matrix of background sample;
3) the decision level rule merges;
According to the Expert opinion zone, divide target area and background area, carry out rule and merge preparation; Target area is designated as
, background area is designated as
; Then carry out the "or" logic in target area, in background area, carry out " with " logic; " from " and "or" concrete operations and different remote sensing instrument between complementation and redundancy closely related, be that the arithmetical logic of reflection multi-sensor information fusion is described; Total target is when removing partial redundance information, give prominence to and embody the message complementary sense that the two sensors foreign matter detects; Carrying out rule fusion concrete grammar at fusion center is:
Obtain fusion results;
Above-mentioned various in,
,
be respectively the visible near-infrared data of 75 wave band and 80 wave band short-wave infrared data;
for the pre-processing image data function;
mean false alarm rate, as the bridge that connects two groups of preliminary rulings;
,
be respectively the preliminary target decision result after the RX abnormality detection of visible near-infrared, short-wave infrared data;
with
mean respectively the bianry image after the visible near-infrared and short-wave infrared RX in target area detects,
with
mean respectively the bianry image after the visible near-infrared and short-wave infrared RX in background area detects,
for the fusion center court verdict.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN2013103933415A CN103456011A (en) | 2013-09-02 | 2013-09-02 | Improved hyperspectral RX abnormal detection method by utilization of complementary information |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN2013103933415A CN103456011A (en) | 2013-09-02 | 2013-09-02 | Improved hyperspectral RX abnormal detection method by utilization of complementary information |
Publications (1)
Publication Number | Publication Date |
---|---|
CN103456011A true CN103456011A (en) | 2013-12-18 |
Family
ID=49738339
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN2013103933415A Pending CN103456011A (en) | 2013-09-02 | 2013-09-02 | Improved hyperspectral RX abnormal detection method by utilization of complementary information |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN103456011A (en) |
Cited By (8)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104062008A (en) * | 2014-06-13 | 2014-09-24 | 武汉理工大学 | Method for removing abnormal spectrums in actually measured spectrum curve with integral measurement considered |
CN104331891A (en) * | 2014-11-04 | 2015-02-04 | 杭州电子科技大学 | Dominant measurement and implicit measurement integrating multi-mode image registering method |
CN109212628A (en) * | 2018-10-22 | 2019-01-15 | 中国科学院声学研究所 | It is a kind of for detecting the detection system of the unconventional target of automobile chassis |
CN109255353A (en) * | 2018-09-12 | 2019-01-22 | 首都师范大学 | A kind of moving target detection method, device, electronic equipment and storage medium |
CN110243805A (en) * | 2019-07-30 | 2019-09-17 | 江南大学 | Fishbone detection method based on Raman high light spectrum image-forming technology |
CN111207838A (en) * | 2020-03-11 | 2020-05-29 | 中南大学 | Molten iron temperature measuring device based on special infrared spectrum wave band |
CN115909113A (en) * | 2023-01-09 | 2023-04-04 | 广东博幻生态科技有限公司 | Method for surveying forestry pests through remote sensing monitoring of unmanned aerial vehicle |
CN116129281A (en) * | 2023-04-18 | 2023-05-16 | 中国人民解放军战略支援部队航天工程大学 | Sub-pixel target detection system for hyperspectral image |
Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001082593A1 (en) * | 2000-04-24 | 2001-11-01 | The Government Of The United States Of America, As Represented By The Secretary Of The Navy | Apparatus and method for color image fusion |
CN1760889A (en) * | 2005-11-03 | 2006-04-19 | 复旦大学 | Method for sorting characters of ground object through interfusion of satellite carried microwave and infrared remote sensing |
CN101908211A (en) * | 2010-08-04 | 2010-12-08 | 北京航空航天大学 | High spectral image fusion method based on variational method |
-
2013
- 2013-09-02 CN CN2013103933415A patent/CN103456011A/en active Pending
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2001082593A1 (en) * | 2000-04-24 | 2001-11-01 | The Government Of The United States Of America, As Represented By The Secretary Of The Navy | Apparatus and method for color image fusion |
CN1760889A (en) * | 2005-11-03 | 2006-04-19 | 复旦大学 | Method for sorting characters of ground object through interfusion of satellite carried microwave and infrared remote sensing |
CN101908211A (en) * | 2010-08-04 | 2010-12-08 | 北京航空航天大学 | High spectral image fusion method based on variational method |
Non-Patent Citations (3)
Title |
---|
徐钰明,于平,郭宝峰,刘锋春: "基于决策级融合的RX高光谱影像异常检测算法", 《红外技术》 * |
徐钰明: "高光谱遥感影像异常检测与分类技术研究", 《杭州电子科技大学硕士学位论文》 * |
陈国平: "《摄影测量与遥感》", 31 March 2011 * |
Cited By (12)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104062008A (en) * | 2014-06-13 | 2014-09-24 | 武汉理工大学 | Method for removing abnormal spectrums in actually measured spectrum curve with integral measurement considered |
CN104062008B (en) * | 2014-06-13 | 2016-04-13 | 武汉理工大学 | A kind of elimination method considering exceptional spectrum in the measured spectra curve of overall tolerance |
CN104331891A (en) * | 2014-11-04 | 2015-02-04 | 杭州电子科技大学 | Dominant measurement and implicit measurement integrating multi-mode image registering method |
CN109255353A (en) * | 2018-09-12 | 2019-01-22 | 首都师范大学 | A kind of moving target detection method, device, electronic equipment and storage medium |
CN109255353B (en) * | 2018-09-12 | 2022-06-28 | 首都师范大学 | Moving target detection method and device, electronic equipment and storage medium |
CN109212628A (en) * | 2018-10-22 | 2019-01-15 | 中国科学院声学研究所 | It is a kind of for detecting the detection system of the unconventional target of automobile chassis |
CN110243805A (en) * | 2019-07-30 | 2019-09-17 | 江南大学 | Fishbone detection method based on Raman high light spectrum image-forming technology |
CN110243805B (en) * | 2019-07-30 | 2020-05-22 | 江南大学 | Fish bone detection method based on Raman hyperspectral imaging technology |
CN111207838A (en) * | 2020-03-11 | 2020-05-29 | 中南大学 | Molten iron temperature measuring device based on special infrared spectrum wave band |
CN115909113A (en) * | 2023-01-09 | 2023-04-04 | 广东博幻生态科技有限公司 | Method for surveying forestry pests through remote sensing monitoring of unmanned aerial vehicle |
CN116129281A (en) * | 2023-04-18 | 2023-05-16 | 中国人民解放军战略支援部队航天工程大学 | Sub-pixel target detection system for hyperspectral image |
CN116129281B (en) * | 2023-04-18 | 2023-06-30 | 中国人民解放军战略支援部队航天工程大学 | Sub-pixel target detection system for hyperspectral image |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
CN103456011A (en) | Improved hyperspectral RX abnormal detection method by utilization of complementary information | |
CN108389188B (en) | Sparse hyperspectral abnormal target detection method | |
CN103226832B (en) | Based on the multi-spectrum remote sensing image change detecting method of spectral reflectivity mutation analysis | |
CN103020978B (en) | SAR (synthetic aperture radar) image change detection method combining multi-threshold segmentation with fuzzy clustering | |
CN107144829A (en) | A kind of efficient laser radar echo signal antinoise method | |
CN106128121B (en) | Vehicle queue length fast algorithm of detecting based on Local Features Analysis | |
CN102506444B (en) | Furnace hearth flame detecting method based on intelligent-control computer vision technology | |
CN105844279A (en) | Depth learning and SIFT feature-based SAR image change detection method | |
CN104867150A (en) | Wave band correction change detection method of remote sensing image fuzzy clustering and system thereof | |
CN109410228A (en) | Internal wave of ocean detection algorithm based on Method Based on Multi-Scale Mathematical Morphology Fusion Features | |
CN102592280B (en) | Hyperspectral image anomaly detection method using multi-window feature analysis | |
CN103394472B (en) | A kind of greening potato based on machine vision detects stage division | |
CN105678047B (en) | Merge empirical mode decomposition noise reduction and the wind field characterizing method of Complex Networks Analysis | |
CN102034337A (en) | System and method for prairie snow disaster remote sensing monitoring and disaster situation evaluation | |
CN101806898A (en) | Hyperspectral remote sensing image target detecting method based on variable end members | |
CN103093432B (en) | Polarized synthetic aperture radar (SAR) image speckle reduction method based on polarization decomposition and image block similarity | |
CN104463808A (en) | High-spectral data noise reduction method and system based on spatial correlation | |
CN103400383A (en) | SAR (synthetic aperture radar) image change detection method based on NSCT (non-subsampled contourlet transform) and compressed projection | |
Qu et al. | Dimensionality reduction and derivative spectral feature optimization for hyperspectral target recognition | |
CN111060455B (en) | Northeast cold-cool area oriented remote sensing image crop marking method and device | |
CN115439759A (en) | Method and device for extracting vegetation in remote sensing image, electronic equipment and medium | |
CN110751667B (en) | Method for detecting infrared dim and small targets under complex background based on human visual system | |
CN113887324A (en) | Fire point detection method based on satellite remote sensing data | |
CN103955711B (en) | A kind of mode identification method in imaging spectral target identification analysis | |
Xiao et al. | Inversion study of cadmium content in soil based on reflection spectroscopy and MSC-ELM model |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
C06 | Publication | ||
PB01 | Publication | ||
C10 | Entry into substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20131218 |