Spatial–spectral segmentation of hyperspectral images for subpixel target detection

Subpixel target detection in hyperspectral images (HSIs) is a problem that emerges in many important civil, military, and environmental applications. The widely used, simple in calculation, matched-filter detector is well-known to exhibit reduced performance when the target-hosting image comprises spectrally diverse, incoherent background regions. We propose to calculate and apply matched-filter subpixel target detection in image regions that exhibit both spectral and spatial coherence. Specifically, our contributions are as follows. (a) We propose the spatial–spectral expectation-maximization (SSEM) algorithm for the segmentation of a target-hosting HSI. (b) We conduct local matched filter computation and application in the SSEM-calculated image segments. The proposed method is accompanied by extensive experimental studies that corroborate its merits.