TY - GEN
T1 - How to fully explore the low-rank property for data recovery of hyperspectral images
AU - Mei, Shaohui
AU - Bi, Qianqian
AU - Ji, Jingyu
AU - Hou, Junhui
AU - Du, Qian
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/11/1
Y1 - 2016/11/1
N2 - The performance of hyperspectral classification is affected by within-class spectral variation since different materials may present similar spectral signatures. In this paper, we investigate how to fully use the low-rank property of hyperspectral images to alleviate spectra variation. Particulary, two effective strategies that explore the low-rank property in local spectral and spatial space are proposed. According to experimental results, we conclude that exploring the low-rank property in local spectral-spatial space can help to alleviate spectral variation and improve the performance of classification obviously for all tested data, while exploring the low-rank property in spatial space is more effective for images presenting large homogeneous areas.
AB - The performance of hyperspectral classification is affected by within-class spectral variation since different materials may present similar spectral signatures. In this paper, we investigate how to fully use the low-rank property of hyperspectral images to alleviate spectra variation. Particulary, two effective strategies that explore the low-rank property in local spectral and spatial space are proposed. According to experimental results, we conclude that exploring the low-rank property in local spectral-spatial space can help to alleviate spectral variation and improve the performance of classification obviously for all tested data, while exploring the low-rank property in spatial space is more effective for images presenting large homogeneous areas.
KW - hyperspectral classification
KW - Low-rank
KW - Robust Principal Component Analysis (R-PCA)
KW - spectral variation
UR - http://www.scopus.com/inward/record.url?scp=85007494695&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2016.7729857
DO - 10.1109/IGARSS.2016.7729857
M3 - 会议稿件
AN - SCOPUS:85007494695
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3314
EP - 3317
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 -