Military object detection using multiple information extracted from hyperspectral imagery

Object detection is a very significant task for a huge range of applications. For example, the detection of military vehicles is very useful for the defense and intelligence. In recent years, hyperspectral imagery (HSI) which is generated by remote sensing systems can provide tremendous information about the spectral characteristics. Due to this characteristic, object detection using HSI becomes hot research topic. In this paper, we propose a strategy for military object detection by extracting multiple information from HSI. Firstly, we generate the superpixels from HSI by principle component analysis (PCA) and k-means clustering. Then, self-similarity method is used to calculate the correlation between each superpixel and the object spectral. At last, the shape information is extracted from the masses which have high correlation value and is used to detect the specific military objectives. Results from HSI demonstrate the benefits of the proposed strategy regarding its effectiveness at detecting specific objectives.