Name
Title | ||
|---|---|---|
Learning group-based sparse and low-rank representation for hyperspectral image classification. |
Abstract | ||
|---|---|---|
Previous studies have demonstrated that the structured sparse representation can yield significant improvements in spectral-spatial hyperspectral classification. However, a dictionary that contains all of the training samples in the sparsity-aware methods is ineffective in capturing the class-discriminative information. This paper makes the first attempt to learn group-based sparse and low-rank representation for improving the dictionary. First, super-pixel segmentation is applied to obtain homogeneous regions that act as spatial groups. Dictionary is then learned with group-based sparse and low-rank regularizations to achieve common representation matrix for the same spatial group. Those group-based sparse and low-rank regularizations facilitate identifying both local and global structure of the hyperspectral image (HSI). Finally, representation matrices of test samples are employed to determine the class labels by a linear support vector machine (SVM). Experimental results on two benchmark HSIs show that the proposed method achieves better performance than the state-of-the-art methods, even with small sample sizes. HighlightsPropose a GSLR method to learn structured dictionary for HSI.Apply fast super-pixel segmentation method to gain spatial groups.Add group-based sparse and low-rank regularizations for dictionary learning.Update representation matrix by IALM and dictionary by BCD.Classify representation matrices of test samples by linear SVM. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.patcog.2016.04.009 | Pattern Recognition |
Keywords | Field | DocType |
Classification,Hyperspectral image (HSI),Dictionary learning,Sparse representation,Low-rank representation | Hyperspectral image classification,Pattern recognition,K-SVD,Matrix (mathematics),Segmentation,Support vector machine,Sparse approximation,Hyperspectral imaging,Artificial intelligence,Sample size determination,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
60 | C | 0031-3203 |
Citations | PageRank | References |
11 | 0.51 | 32 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhi He | 1 | 113 | 11.83 |
| Lin Liu | 2 | 150 | 26.85 |
| Suhong Zhou | 3 | 14 | 4.73 |
| Yi Shen | 4 | 95 | 19.53 |