TY - GEN
T1 - Hyperspectral Imagery Target Detection Using Collaborative Representation with Spectral Variation Extended Dictionary
AU - Xie, Bobo
AU - Zhang, Yifan
AU - Yan, Feng
AU - Feng, Yan
AU - Mei, Shaohui
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Collaborative representation plays an increasingly important role in the field of hyperspectral imagery target detection, resulting in improving detection performance. It is known that, in hyperspectral imagery, both the sensor and external factors (such as weather, illumination and other environmental changes) will lead to the spectral variations within the same type of material, which may greatly affect the detection accuracy. To deal with this issue, a new target detection method using collaborative representation with spectral variation extended dictionary is proposed for hyperspectral imagery in this paper. In the proposed method, an extended dictionary is constructed by enclosing the spectral variation library into the original dictionary, and the following collaborative representation makes the atoms in both original dictionary and spectral variation library contribute to the residual estimation. Compared to the traditional collaborative representation based target detection method, the newly proposed one exhibits better detection performance.
AB - Collaborative representation plays an increasingly important role in the field of hyperspectral imagery target detection, resulting in improving detection performance. It is known that, in hyperspectral imagery, both the sensor and external factors (such as weather, illumination and other environmental changes) will lead to the spectral variations within the same type of material, which may greatly affect the detection accuracy. To deal with this issue, a new target detection method using collaborative representation with spectral variation extended dictionary is proposed for hyperspectral imagery in this paper. In the proposed method, an extended dictionary is constructed by enclosing the spectral variation library into the original dictionary, and the following collaborative representation makes the atoms in both original dictionary and spectral variation library contribute to the residual estimation. Compared to the traditional collaborative representation based target detection method, the newly proposed one exhibits better detection performance.
KW - Collaborative representation
KW - hyperspectral
KW - spectral variation
KW - target detection
UR - http://www.scopus.com/inward/record.url?scp=85077682132&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8898689
DO - 10.1109/IGARSS.2019.8898689
M3 - 会议稿件
AN - SCOPUS:85077682132
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 2280
EP - 2283
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -