Remote sensing image classification exploiting multiple kernel learning

Abstract

We propose a strategy for land use classification which exploits Multiple Kernel Learning (MKL) to automatically determine a suitable combination of a set of features without requiring any heuristic knowledge about the classification task. We present a novel procedure that allows MKL to achieve good performance in the case of small training sets. Experimental results on publicly available datasets demonstrate the feasibility of the proposed approach.

Accepted for publication on the IEEE Geoscience and Remote Sensing letters

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• Land Use and Land Cover Classification Using Deep Learning Techniques