Remote sensing image classification exploiting multiple kernel learning
This addresses land use classification for remote sensing applications, but it appears incremental as it builds on existing MKL methods with a focus on small datasets.
The paper tackled land use classification from remote sensing images by using Multiple Kernel Learning (MKL) to automatically combine features without heuristic knowledge, and introduced a novel procedure to improve performance with small training sets, demonstrating feasibility on public datasets.
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.