Research On Spectral Feature Based Anomaly Target Detection Algorithms In Hyperspectral Imagery

Hyperspectral image is a new class of remote sensing image with the property of imageand spectrum, it has very good expression to the slight differences in surface substance bycontinuous spectrum curve by comparing with multispectral, and make hyperspectral image tohave widely application on spectral classification, spectral unmixing, target detection andanomaly detection etc. In recent years, the anomaly target detection is a hot issue in theapplication field of hyperspectral remote sensing image because of no prior knowledge of thetarget spectral signature is utilized or assumed. That is very useful in reality because thehyperspectral remote sensing image can put the need to detection target from backgroundinformation through spectral features. The existing algorithms are boundedness due to thehigh dimensionality, background interference, nonlinear correlation between bands, andmixed pixels in hyperspectral imagery. Based on the real hyperspectral image, the thesisanalysizes spectral resolution characteristics, band correlation and the background model.Then, based on the spectral characteristics, it is proposed several innovative anomaly targetdetection algorithm by using hyperspectral dimension reduction techniques, feature extractiontechnology and nonlinear kernel machine learning method. The thesis mainly work asfollows:Firstly, the thesis introduces the imaging mechanism of hyperspectral remote sensingimage in brief, and analysizes spectral resolution characteristics, band correlation andbackground model in detail. And the thesis mainly describes the kernel function methods,specially, it describes the gaussian RBF kernel function which is a foundation for the laterresearch work.Secondly, a novel anomaly detection algorithm is proposed for hyperspectral images toresolve the high dimensionality, the nonlinear statistical property and background interference,which is the extended RX algorithm based on spectral dimension transformation and spatialfilter(STSF-RX). Firstly, the maximum noise fraction transform is performed on the originalhyperspectral images, and it obtains MNF transform matrices by setting a SNR threshold, thatthe SNR of their corresponding bands are larger than the threshold. Then, for suppressingbackground interferences, The orthogonal subspace projection is estabished by MNFtransform matrices, and project the hyperspectral data of MNF transform to the orthogonal subspace, and obtain the error data of hyperspectral. In order to concentrate the energy ofdetection targets a few components for the first, the method of principal components analysisis performed on the processing hyperspectral image. Finally, we obtain bands for PCAtransform based on the eigenvalue threshold, the eigenvalue of the bands are larger thanthreshold, and the bands are input to the RX detector. the proposed STSF-RX algorithm areeffectively resolved the high dimensionnality and background interferences.Thirdly, The Kernel RX is the classical anomaly detection algorithm of hyperspectralimages, which exploits nonlinear statistical property between bands. But, the algorithm hasn’tthink about the complexity of spectral and spatial speciality. In addition, the high dimensi-onality and redundancy between bands affect the performance of the KRX algorithm.Therefore, the detection performance of Kernel RX is low. Aiming at the problem, the paperproposes the algorithm of band subsets anomaly detection of hyperspectral image based onfourth order cumulant(KBS-KRX). Firstly, the algorithm divides the original hyperspectralimage to subset of low dimensions according to the correlation coefficient between spectralbands. Then, the error image data is achieved via subset bands were suppressed backgroundinterferences for detection by using orthogonal subspace projection, which is producd by theprincipal component analysis. Based on the data, the feature Information of all band subsetswere extracted by using the principal component analysis, which make the information ofanomaly target concentration on previous bands. At last, the optimal band subsets wereachieved by fourth order cumulant in principal component. In band subsets, the anomalydetection is carried by the kernel RX. KBS-KRX algorithm makes full use of blockingcharacteristics of band correlation in hyperspectral images.Finally, the thesis proposes a novel algorithm for spectral unmixing support vector datadescription(SU-SVDD), it resolves the mixed pixels anomaly detection problem, the proposedalgorithm introduces hyperspectral unmixing into the problem of anomaly detection toseparate target information from complicated background clutter. After spectral unmixing, theerror datum includes abundant target information, and at the same time suppressedbackground interference; then the error datum is mapped into a high dimensional featurespace by a nonlinear SVDD. By exploiting nonlinear information between the spectral bandsof hyperspectral imagery, the anomaly targets can be detected. The proposed algorithm canhighlight anomaly target and highly detect precision.