Name
Title | ||
|---|---|---|
Spectral–Spatial Classification of Hyperspectral Image Based on Low-Rank Decomposition |
Abstract | ||
|---|---|---|
Spectral–spatial classification methods have been proven to be effective in hyperspectral image (HSI) classification. However, most of the methods make use of the correlation in a small neighborhood. In this paper, a novel low-rank decomposition spectral–spatial method (LRDSS) is proposed. LRDSS incorporates the global and local correlation where the global correlation is introduced by discovering the low-dimensional structure in the high-dimensional data, and local correlation is modeled by Markov Random Field (MRF). Specifically, all pixels’ spectrums in a homogeneous area are assumed to have low-dimensional structure. Low rankness is a fine property to characterize the low-dimensional structure and robust principal component analysis (RPCA) is used to extract the low-rank data. Then, the spectral information is obtained by the probabilistic support vector machine (SVM) classifier applied on the low-rank data. Moreover, the MRF models local correlation by encouraging neighboring pixels taking the same label. The maximum a posterior classification is computed by min-cut-based optimization algorithm. The experimental results suggest that LRDSS outperforms the other spectral–spatial classification methods investigated in this paper in terms of classification accuracies. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/JSTARS.2015.2434997 | Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of |
Keywords | Field | DocType |
hyperspectral image (hsi) classification,markov random field (mrf),low-rank decomposition,support vector machine (svm),maximum likelihood estimation,markov processes,image classification,mrf,support vector machines,hyperspectral imaging | Structured support vector machine,Computer vision,Pattern recognition,Markov random field,Support vector machine,Hyperspectral imaging,Robust principal component analysis,Pixel,Artificial intelligence,Probabilistic logic,Classifier (linguistics),Mathematics | Journal |
Volume | Issue | ISSN |
PP | 99 | 1939-1404 |
Citations | PageRank | References |
9 | 0.48 | 32 |
Authors | ||
3 | ||