Multispectral Land Cover Classification Using Averaged Learning Subspace Method

Abstract

For the excellent appearances of Subspace methods in dimension reduction and classification, it is useful to introduce them into classification for multispectral remotely sensed data. This paper presents the first utilization of averaged learning subspace method (ALSM) for land cover classification using Landsat TM image. In particular, a comparative study was made about the classification performances of ALSM and maximum likelihood classification (MLC). ALSM yielded higher classification accuracies than MLC; the overall accuracy of the former algorithm was 99.00% while that of MLC was only 94.99%. The comparison of the classification performance in terms of training set size shows that ALSM outperformed MLC.