TY - JOUR
T1 - Global Spatial and Local Spectral Similarity-Based Manifold Learning Group Sparse Representation for Hyperspectral Imagery Classification
AU - Yu, Haoyang
AU - Gao, Lianru
AU - Liao, Wenzhi
AU - Zhang, Bing
AU - Zhuang, Lina
AU - Song, Meiping
AU - Chanussot, Jocelyn
N1 - Funding Information:
Manuscript received May 11, 2019; revised August 8, 2019; accepted October 1, 2019. Date of publication November 1, 2019; date of current version April 22, 2020. This work was supported by the National Natural Science Foundation of China under Grant 41722108 and Grant 91638201. (Corresponding author: Lianru Gao.) H. Yu and B. Zhang are with the Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China, and also with the College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China (e-mail: [email protected]; [email protected]).
PY - 2020/5
Y1 - 2020/5
N2 - Spectral-spatial framework has been widely applied for hyperspectral image classification task. Some well-established models, such as group sparse representation (GSR), have gained a certain advance but still mainly focus on the usage of local spatial similarity and neglect the nonlocal spatial information. Recently, nonlocal self-similarity (NLSS) has been exploited to support the spatial coherence tasks. However, current NLSS-based methods are biased toward the direct use of nonlocal spatial information as a whole, while the underlying spectral information is not well exploited. In this article, we proposed a novel method to exploit local spectral similarity through nonlocal spatial similarity, with the integration of local spatial consistency in a single framework. Specifically, the proposed approach first exploits the NLSS by searching the nonoverlapped similar patches in defined scopes. Then, spectral similarity is determined locally within the found patches. After that, the found similar data and the original data are fused in a designed pattern. Finally, the GSR-based classifier (GSRC) is applied to process the fused data characterized by the manifold learning algorithm. The experimental results based on three real hyperspectral data sets demonstrate the efficiency of the proposed method, with improvements over the other related nonlocal or local similarity-based methods.
AB - Spectral-spatial framework has been widely applied for hyperspectral image classification task. Some well-established models, such as group sparse representation (GSR), have gained a certain advance but still mainly focus on the usage of local spatial similarity and neglect the nonlocal spatial information. Recently, nonlocal self-similarity (NLSS) has been exploited to support the spatial coherence tasks. However, current NLSS-based methods are biased toward the direct use of nonlocal spatial information as a whole, while the underlying spectral information is not well exploited. In this article, we proposed a novel method to exploit local spectral similarity through nonlocal spatial similarity, with the integration of local spatial consistency in a single framework. Specifically, the proposed approach first exploits the NLSS by searching the nonoverlapped similar patches in defined scopes. Then, spectral similarity is determined locally within the found patches. After that, the found similar data and the original data are fused in a designed pattern. Finally, the GSR-based classifier (GSRC) is applied to process the fused data characterized by the manifold learning algorithm. The experimental results based on three real hyperspectral data sets demonstrate the efficiency of the proposed method, with improvements over the other related nonlocal or local similarity-based methods.
KW - Classification
KW - group sparse representation (GSR)
KW - hyperspectral image
KW - nonlocal self-similarity (NLSS)
UR - http://www.scopus.com/inward/record.url?scp=85084150052&partnerID=8YFLogxK
U2 - 10.1109/TGRS.2019.2947032
DO - 10.1109/TGRS.2019.2947032
M3 - Journal article
AN - SCOPUS:85084150052
SN - 0196-2892
VL - 58
SP - 3043
EP - 3056
JO - IEEE Transactions on Geoscience and Remote Sensing
JF - IEEE Transactions on Geoscience and Remote Sensing
IS - 5
M1 - 8889679
ER -