| Summary: | 碩士 === 國立中央大學 === 資訊工程研究所 === 93 === Recently, hyperspectral images are widely used for target detection in remotely sensed imagery. They take advantage of hundreds of contiguous spectral channels to uncover materials that usually cannot be resolved by multispectal images. However, the ground resolution in hyperspectral imagery is generally larger than the size of targets of interest, under this circumstance target detection must be carried out at subpixel level.
Linear spectral mixture analysis (LSMA) is a widely used technique for subpixel target detection and material classification in hyperspectral image, and least squares unmixing methods are widely used to solve linear mixture problems for material abundance estimation. In this thesis, a weighted least squares (WLS) method is introduced as a generalization. When different weight matrix is applied, a certain detector or classifier will be resulted. Several previous proposed methods have been proven to be versions of WLS methods. For accurate abundance fraction estimation, a fully constrained weighted least squares (FCWLS) approach is developed by combining sum-to-one and nonnegativity constraints. In order to further apply the designed algorithm to unknown image scenes, an unsupervised least squares method is also proposed. Furthermore, several noise estimation methods are introduced, and we also compare the performance of target detection capability.
A serious of computer simulation and real hyperspectral image experiments were conducted in this thesis. The experimental results showed that the noise whitening least squares method in target detection and FCWLS approach in abundance fraction estimation have better performance.
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