Rough-wavelet granular space and classification of multispectral remote sensing image
Applied Soft Computing
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Automatic target recognition (ATR) is an important task in image application. A classifier for the airplane recognition based on the merits of Rough set theory (RST)and Directed Acyclic Graph Support Vector Machines (DAGSVM) is proposed in this paper. RST can mine useful information from a large number of data and generate decision rules without prior knowledge. DAGSVM have better classification performances than other SVMs methods and good capabilities of fault-tolerance and generalization. RST is used as preprocessing step to improve the performances of DAGSVM. Coupled together with RST and DAGSVM techniques they enable the ATR system to identify airplane type on the basis of certain shape feature sets in the images. The methods proposed are compared with general SVMs approach and are shown to perform somewhat better on the remote sensing images.