Mine detection in multispectral imagery using PCA and matched filtering

Hyperspectral data (HS) is increasingly used in target detection applications since it provides both spatial and spectral information about the scene. One of the main challenges in HS data is to handle a large volume of data. On the other hand, mutispectral data provides the information with reduced number of bands. As a result, target detection in multispectral image is more challenging due to lack of information about the objects. In this paper, we presented a new approach to detect land mines in multispectral images. We showed that application of matched filter (MF) to multispectral data is not suitable to detect the targets but after selecting some features based on principal component analysis (PCA) enables it to detect all the targets. We also described a segmentation technique-sliding concentric window (SCW) to extract the land mines from the clutter.