LAND COVER CLASSIFICATION OF ALOS PALSAR DATA USING SUPPORT VECTOR MACHINE

Abstract

Land cover classification is  one  of  the  extensive  used  applications in  the  field  of remote sensing. Recently, Synthetic Aperture Radar (SAR) data has become an increasing popular data source because  its  capability  to  penetrate  through  clouds,  haze,  and  smoke.  This  study  showed  on  an alternative  method  for  land  cover  classification  of  ALOS-PALSAR  data  using  Support  Vector Machine (SVM) classifier. SVM discriminates two classes by fitting an optimal separating hyperplane to the training data in a multidimensional feature space, by using only the closest training samples. In order  to  minimize  the  presence  of  outliers  in  the  training  samples  and  to  increase  inter-class separabilities,  prior  to  classification,  a  training  sample  selection  and  evaluation  technique  by identifying its position in a horizontal vertical–vertical horizontal polarization (HV-HH) feature space was applied. The effectiveness of our method was demonstrated using ALOS PALSAR data (25 m mosaic, dual polarization) acquired in Jambi and South Sumatra, Indonesia. There were nine different classes  discriminated:  forest,  rubber  plantation,  mangrove  &  shrubs  with  trees,  oilpalm  &  coconut, shrubs,  cropland,  bare  soil,  settlement,  and  water.  Overall  accuracy  of  87.79%  was  obtained,  with producer’s accuracies for forest, rubber plantation, mangrove & shrubs with trees, cropland, and water class were greater than 92%.