Anomaly detection in hyperspectral imagery based on maximum entropy and nonparametric estimation

Abstract

This paper presents several maximum entropy and nonparametric estimation detectors (MENEDs) which belong to two categories to detect anomaly targets in hyperspectral imagery. First, probability density of target is estimated using Principle of Maximum Entropy according to the low-probability occurrence of target, which simplifies the generalize likelihood ratio test to merely testing background likelihood. Then considering the high complexity of hyperspectral data, in conjunction with the low-probability occurrence of target, sample-depended multimode model (SDMM) is presented to obtain the probability density of the background. Finally, the estimated probability density of the background is tested to detect targets. The proposed MENEDs greatly depend on hyperspectral data sample, rather than the statistical model, to extract the statistical information, which alleviates statistical model discrepancy and has explicit physical mechanism on detection. Experimental results on visible/near-infrared hyperspectral imagery of type I Operational Modular Imaging Spectrometer (OMIS-I) demonstrate that MENEDs perform better than several known detectors, including RX detector (RXD), normalized RXD (NRXD), modified RXD (MRXD), correlation matrix based NRXD (CNRXD), correlation matrix based MRXD (CMRXD), unified target detector (UTD) and low probability detection (LPD).