TY - GEN
T1 - Background characterization for subpixel target detection
AU - Rotman, Stanley
AU - Ben-Yakar, Sapir
AU - Blumberg, Dan
N1 - Publisher Copyright: © 2017 IEEE.
PY - 2017/12/1
Y1 - 2017/12/1
N2 - When performing point target detection in hyperspectral imagery, one often uses the spectral inverse covariance matrix to whiten the natural noise of the image. Since the cube is not necessarily stationary, we wish to understand when segmentation is worthwhile to provide different covariance matrices for different areas of the cube. Using simulations and several new analytical tools, we propose general guidelines for when segmentation is useful.
AB - When performing point target detection in hyperspectral imagery, one often uses the spectral inverse covariance matrix to whiten the natural noise of the image. Since the cube is not necessarily stationary, we wish to understand when segmentation is worthwhile to provide different covariance matrices for different areas of the cube. Using simulations and several new analytical tools, we propose general guidelines for when segmentation is useful.
KW - Hyperspectral
KW - Segmentation
KW - Target Detection
UR - http://www.scopus.com/inward/record.url?scp=85041839001&partnerID=8YFLogxK
U2 - https://doi.org/10.1109/IGARSS.2017.8127210
DO - https://doi.org/10.1109/IGARSS.2017.8127210
M3 - Conference contribution
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 1344
EP - 1346
BT - 2017 IEEE International Geoscience and Remote Sensing Symposium
T2 - 37th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2017
Y2 - 23 July 2017 through 28 July 2017
ER -