| Index | About | Author | Paper Name | Date of publication | Publication | Update time | Link |
|---|---|---|---|---|---|---|---|
| 1 | The reconstructed pixel of interest has similar spectral characteristics to its four nearest neighbors. The second approach is via a joint sparsity model where hyperspectral pixels in a small neighborhood around the test pixel are simultaneously represented by linear combinations of a few common training samples, which are weighted with a different set of coefficients for each pixel. The proposed sparsity-based algorithm is applied to several real hyperspectral images for classification. | Yi Chen | Hyperspectral Image Classification Using Dictionary-Based Sparse Representation | 12 May 2011 | TGRS | 0923 14:54 | Link |
| 2 | two manifold-based sparse representation algorithms are proposed to exploit the local structure of the test samples in corresponding sparse representations for enforcing smoothness across neighboring samples' sparse representations. Using techniques from regularization and local invariance, two manifold-based regularization terms are incorporated into the ℓ 1 -based objective function. | Yuan Yan Tang | Manifold-Based Sparse Representation for Hyperspectral Image Classification | 22 April 2014 | IGRSS | 1004 13:41 | Link |
| 3 | considering that regions of different scales incorporate the complementary yet correlated information for classification, a multiscale adaptive sparse representation (MASR) model is proposed. The MASR effectively exploits spatial information at multiple scales via an adaptive sparse strategy. The adaptive sparse strategy not only restricts pixels from different scales to be represented by training atoms from a particular class but also allows the selected atoms for these pixels to be varied, thus providing an improved representation. | Leyuan Fang | Spectral–Spatial Hyperspectral Image Classification via Multiscale Adaptive Sparse Representation | 06 May 2014 | TGRS | 1004 17:26 | link |
| 4 | we propose a nonlocal weighted joint sparse representation classification (NLW-JSRC) method to improve the hyperspectral image classification result. In the joint sparsity model (JSM), different weights are utilized for different neighboring pixels around the central test pixel. The weight of one specific neighboring pixel is determined by the structural similarity between the neighboring pixel and the central test pixel, which is referred to as a nonlocal weighting scheme. | Hongyan Zhang | A Nonlocal Weighted Joint Sparse Representation Classification Method for Hyperspectral Imagery | 03 June 2013 | JSTARS | 1004 17:29 | link |
| 5 | two manifold-based sparse representation algorithms are proposed to exploit the local structure of the test samples in corresponding sparse representations for enforcing smoothness across neighboring samples' sparse representations. Using techniques from regularization and local invariance, two manifold-based regularization terms are incorporated into the ℓ 1 -based objective function. | Yuan Yan Tang | Manifold-Based Sparse Representation for Hyperspectral Image Classification | 22 April 2014 | IGRSS | 1004 17:32 | link |
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