Adaptive linear minimum mean square error restoration: influence on hyperspectral detection strategy
International Journal of Remote Sensing
A new feature selection algorithm for multispectral and polarimetric vehicle images
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
A locally adaptable iterative RX detector
EURASIP Journal on Advances in Signal Processing - Special issue on advanced image processing for defense and security applications
A semiparametric model for hyperspectral anomaly detection
Journal of Electrical and Computer Engineering - Special issue on Algorithms for Multispectral and Hyperspectral Image Analysis
| Hi-index | 0.01 |
Multispectral or hyperspectral sensors can facilitate automatic target detection and recognition in clutter since natural clutter from vegetation is characterized by a grey body, and man-made objects, compared with blackbody radiators, emit radiation more strongly at some wavelengths. Various types of data fusion of the spectral-spatial features contained in multiband imagery developed for detecting and recognizing low-contrast targets in clutter appear to have a common framework. A generalized hypothesis test on the observed data is formulated by partitioning the received bands into two groups. In one group, targets exhibit substantial coloring in their signatures but behave either like grey bodies or emit negligible radiant energy in the other group. This general observation about the data generalizes the data models used previously. A unified framework for these problems, which utilizes a maximum likelihood ratio approach to detection, is presented. Within this framework, a performance evaluation and a comparison of the various types of multiband detectors are conducted by finding the gain of the SNR needed for detection as well as the gain required for separability between the target classes used for recognition. Certain multiband detectors become special cases in this framework. The incremental gains in SNR and separability obtained by using what are called target-feature bands plus clutter-reference bands are studied. Certain essential parameters are defined that effect the gains in SNR and target separability