Detection of Yellow Starthistle through Band Selection and Feature Extraction from Hyperspectral Imagery
Document Type
Article
Publication Date
9-2007
Abstract
To effectively display hyperspectral imagery for visualization purposes, the three RGB channels should be selected or extracted from a hyperspectral image under the criteria of maximum information or maximum between-class separability. Seven band selection (OIF, SI, CI, divergence, transformed divergence, B-distance, JM-distance) and five feature extraction (principal component analysis, linear discriminant analysis, class-based PCA, segmented PCT (SPCT), independent component analysis) methods and their variations are examined and compared using CASI hyperspectral imagery with the goal of detecting Centaurea solstitialis (yellow starthistle or YST), an invasive weed, in an annual grassland in California. Three indicators, information index (Infodex), separability index (Sepadex) and average correlation coefficient (ACC) are proposed to evaluate the quality of the generated images. The results suggest that both the combination of the three SPCT channels and the combination of the second PCA channel with the positive and negative of the first LDA channels (PCA2, LDA1, -LDA1) can enhance our ability to visualize the distribution of YST in contrast to the surrounding vegetation.
Was this content written or created while at USF?
No
Citation / Publisher Attribution
Photogrammetric Engineering & Remote Sensing, v. 73, issue 9, p. 1005-1015
Scholar Commons Citation
Miao, Xin; Gong, Peng; Swope, Sarah; Pu, Ruiliang; Carruthers, Raymond; and Anderson, Gerald L., "Detection of Yellow Starthistle through Band Selection and Feature Extraction from Hyperspectral Imagery" (2007). School of Geosciences Faculty and Staff Publications. 379.
https://digitalcommons.usf.edu/geo_facpub/379
