Share Email Print
cover

Proceedings Paper

Constrained subpixel target detection for hyperspectral imagery
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Target detection in remotely sensed images can be conducted spatially, spectrally or both. The difficulty of detecting targets in remotely sensed images with spatial image analysis arises from the fact that the ground sampling distance is generally larger than the size of the targets of interest in which case targets are embedded in a single pixel and cannot be detected spatially. Under this circumstance target detection must be carried out at subpixel level and spectral analysis offers a valuable alternative. This paper compares two constrained approaches for subpixel detection of targets in remote sensing images. One is a target abundance-constrained approach, referred to as the nonnegatively constrained least squares (NCLS) method. It is a constrained least squares linear spectral mixture analysis method which implements a nonnegatively constraint on the abundance fractions of targets of interest. A common drawback of linear spectral mixture analysis based methods is the requirement for prior knowledge of the endmembers present in an image scene. In order to mitigate this drawback, the NCLS method is extended to create an unsupervised approach, referred to as the unsupervised nonnegatively constrained least present in the image scene. The second approach is a target signature-constrained method, called the constrained energy minimization (CEM) method. It constrains the desired target signature with a specific gain while minimizing effects caused by other unknown signatures. Data from the HYperspectral Digital Imagery Collection Experiment (HYDICE) sensor are used to compare the performance of these methods.

Paper Details

Date Published: 13 July 2000
PDF: 11 pages
Proc. SPIE 4048, Signal and Data Processing of Small Targets 2000, (13 July 2000); doi: 10.1117/12.391995
Show Author Affiliations
Chein-I Chang, Univ. of Maryland/Baltimore County (United States)
Daniel C. Heinz, Univ. of Maryland/Baltimore County (United States)


Published in SPIE Proceedings Vol. 4048:
Signal and Data Processing of Small Targets 2000
Oliver E. Drummond, Editor(s)

© SPIE. Terms of Use
Back to Top