Detecting enemy’s targets and being undetectable play increasingly important roles in modern warfare. Hyperspectral images can provide large spectral range and high spectral resolution, which are invaluable in discriminating between camouflaged targets and backgrounds. As supervised classification requires prior knowledge which cannot be acquired easily, unsupervised classification usually is adopted to process hyperspectral images to detect camouflaged target. But one of its drawbacks—low detecting accuracy confines its application for camouflaged target detecting. Most research on the processing of hyperspectral image tends to focus exclusively on spectral domain and ignores spatial domain. However current hyperspectral image provides high spatial resolution which contains useful information for camouflaged target detecting. A new method combining spectral and spatial information is proposed to increase the detecting accuracy using unsupervised classification. The method has two steps. In the first step, a traditional unsupervised classifier (i.e. K-MEANS, ISODATA) is adopted to classify the hyperspectral image to acquire basic classifications or clusters. During the second step, a 3×3 model and spectral angle mapping are utilized to test the spatial character of the hyperspectral image. The spatial character is defined as spatial homogeneity and calculated by spectral angle mapping. Theory analysis and experiment shows the method is reasonable and efficient. Camouflaged targets are extracted from the background and different camouflaged targets are also recognized. And the proposed algorithm outperforms K-MEANS in terms of detecting accuracy, robustness and edge’s distinction. This paper demonstrates the new method is meaningful to camouflaged targets detecting.
Hyperspectral imaging technology shows great potential for detection of camouflaged targets. Hyperspectral imagery provides fully registered high resolution spatial and spectral images that are invaluable in discriminating between camouflaged targets and backgrounds. However, current research on the processing of hyperspectral imagery tends to focus exclusively on spectral dimension, and thus the spatial and spectral information are not treated simultaneously. In order to get the shape of target, we have developed a new method based on mathematical morphology for edge detection in hyperspectal data. On the basis of analysis, spectral angle distance is utilized in extended morphological operations, which combine spectral and spatial information together. Then operator of edge extraction is used to extract the edge of target. Three major contributions are presented for detection of camouflaged targets in the paper. Firstly, in spectral domain, the best wave band to distinguish camouflaged targets and background is obtained by subtracting reflex intensity of targets and background. Secondly, we confirm the effectiveness of spectral angle mapping in detecting camouflaged targets, which lays a foundation for the use of spectral angle distance on the following study. Finally, as the prior knowledge about extended morphological operations and edge extraction known already, we successful extract the edge of the camouflaged target which is collected by a hyperspectral imager based on AOTF in the VIS-SWIR region.