Geometric basis-vector selection methods and subpixel target detection as applied to hyperspectral imagery

International Geoscience and Remote Sensing Symposium (IGARSS)

Volumn 5, Issue , 2004, Pages 3211-3214

  Bajorski, Peter ^a   Ientilucci, Emmett J ^a  

^a ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

Author keywords

Basis Vector; Endmember; HYDICE; Hyperspectral; Subpixel Target Detection

Indexed keywords

APPROXIMATION THEORY; CONSTRAINT THEORY; GEOMETRY; ILLUMINATING ENGINEERING; MATHEMATICAL MODELS; SPECTRUM ANALYSIS; VECTORS;

BASIS VECTOR; ENDMEMBER; HYDICE; HYPERSPECTRAL; SUBPIXEL TARGET DETECTION;

IMAGE ANALYSIS;

EID: 15944415063     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (4)

1. P. Bajorski, E. Ientilucci, and J. Schott. "Comparison of Basis-Vector Selection Methods for Target and Background Subspaces as Applied to Subpixel Target Detection" (to appear in Proc. SPIE, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery X, Orlando, FL, April 2004).
2. G. Healey and D. Slater. Models and methods for automated material identification in hyperspectral imagery acquired under unknown illumination and atmospheric conditions. IEEE Transactions on Geoscience and Remote Sensing, 37 (6), pp. 2706-2717, November 1999.
3. D. Manolakis. Overview of algorithms for hyperspectral target detection: theory and practice. In Proc. SPIE Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery VIII, volume 4725, pp. 202-251, Orlando, FL, April 2002.
4. D. Manolakis and G. Shaw. Detection algorithms for hyperspectral imaging applications. IEEE Signal Processing Magazine, 19 (1):29-43, January 2002.