Anomaly detection in hyperspectral imagery based on maximum entropy and nonparametric estimation
Pattern Recognition Letters
Evaluating subpixel target detection algorithms in hyperspectral imagery
Journal of Electrical and Computer Engineering - Special issue on Algorithms for Multispectral and Hyperspectral Image Analysis
| Hi-index | 35.68 |
The fully adaptive hypothesis testing algorithm developed by I.S. Reed and X. Yu (1990) for detecting low-contrast objects of unknown spectral features in a nonstationary background is extended to the case in which the relative spectral signatures of objects can be specified in advance. The resulting background-adaptive algorithm is analyzed and shown to achieve robust spectral feature discrimination with a constant false-alarm rate (CFAR) performance. A comparative performance analysis of the two algorithms establishes some important theoretical properties of adaptive spectral detectors and leads to practical guidelines for applying the algorithms to multispectral sensor data. The adaptive detection of man-made artifacts in a natural background is demonstrated by processing multiband infrared imagery collected by the Thermal Infrared Multispectral Scanner (TIMS) instrument