Amin Zehtabian - Tarbiat Modares University
Hassan Ghassemian

Research on remote sensing image classification approaches has gained momentum during the past decades, especially since the availability of high resolution andmulti/hyper-spectral imagery capabilities. The Maximum Likelihood (ML) based classification methods have been also extensively studied and their effectiveness in this area is clear for researchers. The presented paper is dealing with a novel MLbased multi-spectral classification method which is robustlydeveloped to resist against the huge amounts of additive noises which may infect the remotely sensed data. The proposedapproach inventively utilizes a well-organized Singular Value Decomposition (SVD) denoising schema for reducing the effect of noise from the received multi-band data and then the MLclassifier is applied to the noise-reduced dataset. Indeed since obtaining adequate number of training samples may be costly oreven impossible in many cases and because of the Hughes phenomenon, we also propose to apply PCA for favorablyreducing the number of bands to gain a more accurate performance in cases in which few limited training samples are available. The achieved results clearly imply the effectiveness and prominence of the proposed method for multi-spectral image classification especially in the noisy environments as well as in conditions where small amount of training samples is available.

Remote Sensing; Multi-Spectral Image Classification;SVD; PCA; Maximum Likelihood