Target Detection On Hyperspectral Imagery Based On Transformation Of Spectral Dimensions

Hyperspectral imaging is a new and growing technology with the development of airborne and spaceborne remote sensing from the 80s'of 20th Century. Each pixel in the hyperspectral image is an observation vector and it represents a reflectance spectrum of the materials in the ground area in the Instantaneous Field of View. The Target Detection on hyperspectral imagery is a technique by which the information is obtained based on the transform of spectral dimensions. It can help the expert in many kinds of fields to find the target in the image on the one hand. On the one hand it can replace the Expert Decision to finish the target detection tasks by the method of Artificial Intelligence.It is a flexible remote sensing procedure to detect the specified targets through hyperspectral images. This dissertation first introduces each detail of this procedure to validate the feasibility of target detection by hyperspectral remote sensing. Many kinds of decorrelation transforms and subspace projection transforms both in Spectral Dimensions are studied for the purpose to understand the hyperspectral analysis in Spectral Dimensions well. And then based on the research of scholars in many fields, this dissertation introduces the contents about mathematical models, design procedures, category and performance estimations of different detectors. Then pointing at two different mathematical models, the probability statistics model and the subspace model, the discussions, designs and experiments to target detection algorithms are opened out.The main fruits of this dissertation are as follows:1. To affirm the equivalence of the MNF and NAPC transform, they were discussed as two different mathematical transform methods and finally this conclusion is proved. Then it is proved that how the sample classes are scattered have great influences on classification accuracy when using MNF transform. An improved MNF method, in which the Noise Covariance Matrix estimation method is optimized, is introduced for this problem. 2. For the purpose that to estimate the performance of different hyperspectral target detectors, this article sum up the detector performance parameters of different fields, such as the Probability of False Alarm and the Probability of Detection, the character of Constant False-Alarm-Rate, Signal to Interference-plus-Noise Ratio ROC curve and so on. And many detectors are estimated in accordance with these performance parameters through experiments.3. The detector algorithms based on the probability statistics model are uniformly described in the whitened space. Pointing at how to select a proper decision sufface between targets and backgrounds in the whitened space, the hypothesis that the background submits to normal distribution is denied in this dissertation, and two algorithms based on Elliptically Contoured Distribution (ECD) function are introduced. They are named ECD Detector with Hyperbola Threshold and ECD Detector with Parabola Threshold. They both perform better than ACE detector on Dection Probability when testified by expermiments4. The detector algorithms based on the subspace model are uniformly described in the Euclidean space in which some target detector are proved equivalent. And a new Generalized Likelihood-Ratio algorithm based on Oblique Subspace Projection is presented. Testified by expermiments, this detector performs well than GLRT detector when the vectors of the background signature are not enough to describe background subspace.5. We try to use the MNF transform in the CEM detector and OSP detector. And find that the Eigenvector of MNF can describe the background subspace accurately, but not the image subspace. So MNF can be used in the detectors which are based on background subspace projection. A new Unsupervised OSP algorithm based on MNF transform is presented.