TY - JOUR
T1 - Automatic subpixel target detection for multispectral remotely sensed imagery
AU - Ren, Hsuan
PY - 2004
Y1 - 2004
N2 - Least square unmixing approach has been successfully applied to hyperspectral remotely sensed images for subpixel target detection. It can detect target with size less than a pixel by estimating its abundance fraction resident in each pixel. In order for the this approach to be effective, the number of bands must be larger than or equal to that of signatures to be classified, i.e., the number of equations should be no less than the number of unknowns. This ensures that there are sufficient dimensions to accommodate orthogonal projections resulting from the individual signatures. It 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 might be greater than the number of bands. In order to relax this constraint, an extension of the least square approach is presented. With a set of least square filters that are nonlinearly combined, endmember detection for multispectral images can be realized. Furthermore, to detect targets in unknown background is a greater challenge. That is the well known Automatic Target Recognition (ATR) programs. In this paper, we also proposed a Multispectral Target Generation Process (MTGP) that will automatic search for potential targets in the image scene. The effectiveness of the proposed method is evaluated by SPOT images. The experimental results show significantly improves in classification performance than Orthogonal Subspace Projection (OSP) and Automatic Target Detection and Classification Algorithm (ATDCA).
AB - Least square unmixing approach has been successfully applied to hyperspectral remotely sensed images for subpixel target detection. It can detect target with size less than a pixel by estimating its abundance fraction resident in each pixel. In order for the this approach to be effective, the number of bands must be larger than or equal to that of signatures to be classified, i.e., the number of equations should be no less than the number of unknowns. This ensures that there are sufficient dimensions to accommodate orthogonal projections resulting from the individual signatures. It 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 might be greater than the number of bands. In order to relax this constraint, an extension of the least square approach is presented. With a set of least square filters that are nonlinearly combined, endmember detection for multispectral images can be realized. Furthermore, to detect targets in unknown background is a greater challenge. That is the well known Automatic Target Recognition (ATR) programs. In this paper, we also proposed a Multispectral Target Generation Process (MTGP) that will automatic search for potential targets in the image scene. The effectiveness of the proposed method is evaluated by SPOT images. The experimental results show significantly improves in classification performance than Orthogonal Subspace Projection (OSP) and Automatic Target Detection and Classification Algorithm (ATDCA).
KW - Automatic Target Recognition (ATR)
KW - Band number constraint (BNC)
KW - Hyperspectral
KW - Least square
KW - Multispectral
KW - Multispectral Target Generation Process (MTGP)
KW - Orthogonal Subspace Projection (OSP)
UR - http://www.scopus.com/inward/record.url?scp=16644388038&partnerID=8YFLogxK
U2 - 10.1117/12.571719
DO - 10.1117/12.571719
M3 - 會議論文
AN - SCOPUS:16644388038
SN - 0277-786X
VL - 5584
SP - 194
EP - 201
JO - Proceedings of SPIE - The International Society for Optical Engineering
JF - Proceedings of SPIE - The International Society for Optical Engineering
M1 - 25
T2 - Chemical and Biological Standoff Detection II
Y2 - 27 October 2004 through 28 October 2004
ER -