# Remote sensing image classification exploiting multiple kernel learning

Source: [Remote sensing image classification exploiting multiple kernel learning](<https://www.sota2.com/research/paper/68fa62e91e209131ce9fbc5c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:29:18.716Z

Authors: Claudio Cusano, Paolo Napoletano, Raimondo Schettini

Published: 2014-10-20T17:15:50.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1410.5358>)

[PDF](<https://arxiv.org/pdf/1410.5358.pdf>)