Hyperspectral image classification based on joint sparsity model with low-dimensional spectral–spatial features

Pin Wang; Sha Xu; Yongming Li; Jie Wang; Shujun Liu

doi:10.1117/1.JRS.11.015010

6 February 2017 Hyperspectral image classification based on joint sparsity model with low-dimensional spectral–spatial features

Pin Wang, Sha Xu, Yongming Li, Jie Wang, Shujun Liu

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 11, Issue 1, 015010 (February 2017). https://doi.org/10.1117/1.JRS.11.015010

Abstract

We propose a hyperspectral image (HSI) classification method combining low-dimensional spectral–spatial features with joint sparsity model (JSM). First, for the high-dimensional data sets, we introduced image fusion for feature reduction. Then fast bilateral filtering is adopted to exploit spatial features, which will be combined with the original spectral features for classification. Based on the low-dimensional spectral–spatial features, we utilize JSM to serve as a classifier. Considering the strong relationship between the neighboring pixels in HSI, this model can achieve a promising performance by exploiting regional spectral–spatial information. Overall accuracies (with 10% and 2% training samples) of the proposed method are 97.84% and 97.52% for the Indian Pines image and University of Pavia image. Experimental results on different HSI data sets show that the proposed method shows outstanding performance in terms of classification accuracy and computational efficiency.

Citation Download Citation

Pin Wang, Sha Xu, Yongming Li, Jie Wang, and Shujun Liu "Hyperspectral image classification based on joint sparsity model with low-dimensional spectral–spatial features," Journal of Applied Remote Sensing 11(1), 015010 (6 February 2017). https://doi.org/10.1117/1.JRS.11.015010

Received: 29 November 2016; Accepted: 17 January 2017; Published: 6 February 2017

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