TY - GEN
T1 - Multispectral subpixel detection using least square unmixing
AU - Ren, Hsuan
AU - Chang, Yang Lang
PY - 2006
Y1 - 2006
N2 - Many approaches have been developed for subpixel target detection in the past, and least square unmixing is one of the most widely used methods. It can detect subpixel target by estimating its abundance fraction resident in each pixel. This method has been successfully applied in hyperspectral remotely sensed images, but in order for this approach to be effective, the number of bands must be no less than that of signatures to be classified. This constraint is known as Band Number Constraint (BNC). Such inherent constraint is not an issue for hyperspectral images since they generally have hundreds of bands, which is more than the number of signatures resident within images. However, this may not be true for multispectral images where the number of signatures to be classified is greater than the number of bands. In this paper, instead of increasing the number of bands, we decrease the number of signatures by selecting part of materials applied to least square approach, and then those detection results are nonlinearly combined for endmember detection. It can be viewed as an extension of the least square approach and the experimental results showed it can successfully detect all endmembers.
AB - Many approaches have been developed for subpixel target detection in the past, and least square unmixing is one of the most widely used methods. It can detect subpixel target by estimating its abundance fraction resident in each pixel. This method has been successfully applied in hyperspectral remotely sensed images, but in order for this approach to be effective, the number of bands must be no less than that of signatures to be classified. This constraint is known as Band Number Constraint (BNC). Such inherent constraint is not an issue for hyperspectral images since they generally have hundreds of bands, which is more than the number of signatures resident within images. However, this may not be true for multispectral images where the number of signatures to be classified is greater than the number of bands. In this paper, instead of increasing the number of bands, we decrease the number of signatures by selecting part of materials applied to least square approach, and then those detection results are nonlinearly combined for endmember detection. It can be viewed as an extension of the least square approach and the experimental results showed it can successfully detect all endmembers.
UR - http://www.scopus.com/inward/record.url?scp=34948896617&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2006.708
DO - 10.1109/IGARSS.2006.708
M3 - 會議論文篇章
AN - SCOPUS:34948896617
SN - 0780395107
SN - 9780780395107
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 2743
EP - 2745
BT - 2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2006 IEEE International Geoscience and Remote Sensing Symposium, IGARSS
Y2 - 31 July 2006 through 4 August 2006
ER -