TY - GEN
T1 - Double reweighted sparse regression for hyperspectral unmixing
AU - Wang, Rui
AU - Li, Heng Chao
AU - Liao, Wenzhi
AU - Pizurica, Aleksandra
N1 - Funding Information:
This work was supported by China Scholarship Council, the FWO project G037115N: Data fusion for image analysis in remote sensing and the National Natural Science Foundation of China under Grant 61371165.
Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - Spectral unmixing is an important technology in hyperspectral image applications. Recently, sparse regression is widely used in hyperspectral unmixing. This paper proposes a double reweighted sparse regression method for hyperspectral unmixing. The proposed method enhances the sparsity of abundance fraction in both spectral and spatial domains through double weights, in which one is used to enhance the sparsity of endmembers in the spectral library, and the other to improve the sparseness of abundance fraction of every material. Experimental results on both synthetic and real hyperspectral data sets demonstrate effectiveness of the proposed method both visually and quantitatively.
AB - Spectral unmixing is an important technology in hyperspectral image applications. Recently, sparse regression is widely used in hyperspectral unmixing. This paper proposes a double reweighted sparse regression method for hyperspectral unmixing. The proposed method enhances the sparsity of abundance fraction in both spectral and spatial domains through double weights, in which one is used to enhance the sparsity of endmembers in the spectral library, and the other to improve the sparseness of abundance fraction of every material. Experimental results on both synthetic and real hyperspectral data sets demonstrate effectiveness of the proposed method both visually and quantitatively.
KW - Hyperspectral unmixing
KW - double weights
KW - sparse regression
UR - http://www.scopus.com/inward/record.url?scp=85007441263&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2016.7730822
DO - 10.1109/IGARSS.2016.7730822
M3 - Conference contribution
AN - SCOPUS:85007441263
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 6986
EP - 6989
BT - 2016 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 36th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2016
Y2 - 10 July 2016 through 15 July 2016
ER -