The Impacts of Decomposition Levels in Wavelet Transform on Anomaly Detection from Hyperspectral Imagery
분야
자연과학 > 기타(자연과학)
저자
( Hee Young Yoo ) , ( No Wook Park )
발행기관
대한원격탐사학회
간행물정보
대한원격탐사학회지 2012년, 제28권 제6호, 623~632페이지(총10페이지)
파일형식
1r600931.pdf [무료 PDF 뷰어 다운로드]
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    영문초록
    In this paper, we analyzed the effect of wavelet decomposition levels in feature extraction for anomaly detection from hyperspectral imagery. After wavelet analysis, anomaly detection was experimentally performed using the RX detector algorithm to analyze the detecting capabilities. From the experiment for anomaly detection using CASI imagery, the characteristics of extracted features and the changes of their patterns showed that radiance curves were simplified as wavelet transform progresses and H bands did not show significant differences between target anomaly and background in the previous levels. The results of anomaly detection and their ROC curves showed the best performance when using the appropriate sub-band decided from the visual interpretation of wavelet analysis which was L band at the decomposition level where the overall shape of profile was preserved. The results of this study would be used as fundamental information or guidelines when applying wavelet transform to feature extraction and selection from hyperspectral imagery. However, further researches for various anomaly targets and the quantitative selection of optimal decomposition levels are needed for generalization.
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