This dissertation develops new techniques to reduce computational complexity for hyperspectral remote sensing image analysis. Specific techniques are applied with regards to different applications of hyperspectral imagery, i.e., classification, target detection. The contribution of this dissertation can be summarized as follows. 1. A time efficient version combining multiple collaborative representations model is proposed for hyperspectral image classification. Collaborative representation (CR) can be implemented either with a dictionary containing training samples of all-classes or class-specific. A collaborative representation optimized classifier with Tikhonov regularization (CROCT) is proposed to avoid the redundant operations in all-class and class-specific versions. 2. An efficient probabilistic collaborative representation is presented for hyperspectral image classification. Its performance is evaluated on different types of spatial features of hyperspectral imagery including shape feature (i.e., extended multi-attribute feature), global feature (i.e., Gabor feature), and local feature (i.e., Local Binary Pattern). Experimental results show the probabilistic collaborative representation based classifier (PROCRC) has excellent performance in terms of both accuracy and computational cost compared with the original CRC and regularized versions of CRC. 3. Fast nonlinear classification and an explicit kernel approach are built for multispectral and hyperspectral imagery respectively to improve the kernel version of collaborative representation based algorithms. Experimental results show that using artificial bands generated from a simple band ratio function can yield better classification accuracy than the nonlinear kernel method and also reduce computational cost. In addition, the explicit kernel mapping approach can yield high accuracy as the original kernel versions of CR-based algorithms but with similarly low computational cost as in the original linear CRC classifiers. 4. Efficient band selection approaches are proposed for hyperspectral target detection. A maximum-sub-maximum ratio (MSR) metric has been applied for band selection, which can well gauge the target background separation. Efficient evolutionary searching methods such as particle swarm optimization and firefly algorithm are used in conjunction with maximum-sub-maximum ratio metric for band selection. Experimental results show that the proposed band selection approach can select a small band set while yielding similar detection performance compared with using all the original bands.
| Identifer | oai:union.ndltd.org:MSSTATE/oai:scholarsjunction.msstate.edu:td-2835 |
| Date | 03 May 2019 |
| Creators | Xu, Yan |
| Publisher | Scholars Junction |
| Source Sets | Mississippi State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Theses and Dissertations |
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