A SVM and k-NN Restricted Stacking to Improve Land Use and Land Cover Classification

Abstract

Land use and land cover (LULC) maps are remote sensing products that are used to classify areas into different landscapes. The newest techniques have been applied to improve the final LULC classification and most of them are based on SVM classifiers. In this paper, a new method based on a multiple classifiers ensemble to improve LULC map accuracy is shown. The method builds a statistical raster from LIDAR and image fusion data following a pixel-oriented strategy. Then, the pixels from a training area are used to build a SVM and k-NN restricted stacking taking into account the special characteristics of spatial data. A comparison between a SVM and the restricted stacking is carried out. The results of the tests show that our approach improves the results in the context of the real data from a riparian area of Huelva (Spain).