Name
Abstract | ||
|---|---|---|
This letter presents a multiscale low-rank decomposition (MSLRD) method to extract multiscale spatial structures from hyperspectral images. The MSLRD assumes that ground objects have divergent characteristics in changing spatial scales. It decomposes each band image into a series of block-wise matrices, where these low-rank blocks take detailed spatial structures at multiple scales. It formulates the low-rank matrix decomposition problem into minimizing the ranks of all block matrices and adopts the alternative direction of the multiplier method to optimize it. Experiments on Indian Pines and Pavia University data sets show that the MSLRD can greatly improve the classification performance of regular classification on spectral features (i.e., all bands) and perform better than five state-of-the-art spatial feature extraction methods. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/LGRS.2020.3034631 | IEEE GEOSCIENCE AND REMOTE SENSING LETTERS |
Keywords | DocType | Volume |
Feature extraction, Matrix decomposition, Hyperspectral imaging, Data mining, Sparse matrices, Electronic mail, Support vector machines, Classification, hyperspectral imagery (HIS), multiscale low-rank decomposition (MSLRD), spatial features | Journal | 19 |
ISSN | Citations | PageRank |
1545-598X | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiwei Sun | 1 | 1 | 5.75 |
| Wenjing Shao | 2 | 0 | 0.34 |
| Jiangtao Peng | 3 | 1 | 5.42 |
| Gang Yang | 4 | 2 | 6.10 |
| Xiangchao Meng | 5 | 10 | 5.20 |
| Qian Du | 6 | 9 | 17.04 |