Superpixel Based Hyperspectral Target Detection

Using the spectral signature of a target by means of matching the signature with the pixels of an acquired hyperspectral image has been proven as an effective way of classifying hyperspectral pixels in most of the proposed methods in hyperspectral image analysis. A disadvantage of these methods is however to use only the spectral characteristics of pixels for detection while ignoring the spatial relations between the neighbouring pixels. In this paper, we propose a hyperspectral target detection method which uses also the spatial neigboorhood information as well as the spectral characteristics of hyperspectral pixels. To this end, we first utilize superpixelization method [1] to describe the neigborhood relation between the hyperspectral pixels, which has been previously developed and proved to be better compared to a pioneer state-of-the-art superpixel algorithm, SLIC [2]. Second, we investigate the best representatives for superpixels among different alternatives, such as centroids, medoid and mean, and modify the well-known hyperspectral target detection algorithm using orthogonal subspace projection, DTDCA [3], appropriately for superpixels. The improvements of the proposed approach over DTDCA in terms of the detection and false detection rates are verified on real hyperspectral images taken from wheat and corn fields with a VNIR camera.