TY - GEN
T1 - Unsupervised nearest regularized subspace for anomaly detection in hyperspectral imagery
AU - Li, Wei
AU - Du, Qian
PY - 2013
Y1 - 2013
N2 - A method of unsupervised nearest regularized subspace is proposed for anomaly detection in hyperspectral imagery. Based on a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination, for which the weight vector is calculated by distance-weighted Tikhonov regularization. Proposed detector returns the similarity measurement between the testing pixel and its approximation. Experimental results for real hyperspectral data of proposed approach are demonstrated and compared to other traditional detection techniques.
AB - A method of unsupervised nearest regularized subspace is proposed for anomaly detection in hyperspectral imagery. Based on a dual window, an approximation of each testing pixel is a representation of surrounding data via a linear combination, for which the weight vector is calculated by distance-weighted Tikhonov regularization. Proposed detector returns the similarity measurement between the testing pixel and its approximation. Experimental results for real hyperspectral data of proposed approach are demonstrated and compared to other traditional detection techniques.
KW - Anomaly Detection
KW - Hyperspectral Imagery
KW - Tikhonov regularization
UR - http://www.scopus.com/inward/record.url?scp=84894249820&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2013.6721345
DO - 10.1109/IGARSS.2013.6721345
M3 - Conference contribution
AN - SCOPUS:84894249820
SN - 9781479911141
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1055
EP - 1058
BT - 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013 - Proceedings
T2 - 2013 33rd IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2013
Y2 - 21 July 2013 through 26 July 2013
ER -