Land cover mapping classification based on multi Restricted Boltzmann machines and Support Vector Machines

Abstract

In this paper, we introduced a land cover mapping algorithm that combines for unsupervised and supervised classification techniques, namely, the Restricted Boltzmann machines (RBMs) and Support Vector Machines (SVMs). The idea is to take advantage of unsupervised classifications that can segment an image into regions without any training samples, and the supervised classification that can identify the underlying land cover class for each segment. The QUICKBIRD satellite image data covering a part of Kasetsart University was used for evaluation. Experimental results showed that proposed method can classify image data successfully, and texture information can increase the classification performance of remote sensing classification.