On the use of Support Vector Machines for land cover analysis with L-band SAR data

Abstract

This study investigates a new technique for land cover analysis by means of the Support Vector Machines. Intrinsic spatial variability within SAR images, beyond that caused by speckle, is of high interest for land cover characterization and classification. However, its use is still an ongoing issue due to its complex multi-scale nature. On the other hand, classification algorithms based on statistical learning methods such as the supervised Support Vector Machines (SVM) approach are implemented in a wide range of data mining applications. SVM can also be used as a technique for feature selection. In this paper, a new tool using the Recursive Feature Elimination SVM-based process (SVM-RFE) and the textural Haralick's parameters is introduced. The real contribution of textures within the land cover classification can be understood. A small set of textural parameters is determined at local scale while being optimal for the land cover discrimination. In this study, orthorectified 50m resolution data acquired by the L-band PALSAR/ALOS sensor are used.