Hyperspectral Data Processing: Algorithm Design and Analysis

33.4 Anomaly Detection

Anomaly detection received little interest in early days in remote sensing. There are several reasons attributed to this incident. One is due to applications which are mainly focused on geographic information processing such as land cover/use classification. Another is the use of multispectral imagery which has very low spatial and spectral resolutions. As a consequence, anomalies may be very much likely either embedded or mixed with other material substances in a single pixel vector. Under such circumstances, anomalies may have been contaminated or smeared by other dominant substances. Third, most remote sensing image processing techniques developed for such applications are spatial domain-based methods which are designed to take advantage of spatial correlation to perform image analysis. Since anomalies usually appear in a very limited spatial extent, such spatial domain-based methods can hardly capture their existence. Finally, anomalies do not provide much information to multispectral image analysts who are more interested in geographic information rather than target information. However, the advances of hyperspectral imaging sensors have revolutionized the way to process multispectral imagery. Due to the very high spectral resolution provided by hyperspectral sensors, many subtle substances that are generally unknown a priori or cannot be identified by visual assessment can now be uncovered for data analysis. On many occasions such substances are most ...