Multi-Objects Detection in Remote Sensing Images Using Multiple Kernel Learning

Abstract:

The objective of this work is multiple objects detection in remote sensing images. Many classifiers have been proposed to detect military objects. In this paper, we demonstrate that linear combination of kernels can get a better classification precision than product of kernels. Starting with base kernels, we obtain different weights for each class through learning. Experiment on Caltech-101 dataset shows the learnt kernels yields superior classification results compared with single-kernel SVM. While such a powerful classifier act as a sliding-window detector to search planes in images collected from Google Earth, results shows the effectiveness of using MKL detector to locate military objects in remote sensing images.

• Periodical: Advanced Materials Research (Volumes 532-533)
• Pages: 1258-1262
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.532-533.1258
• Online since: June 2012
• Authors: Xiang Juan Li, Hao Sun, Xin Wei Zheng, Xian Sun, Hong Qi Wang
• Keywords: Feature Extraction, Multiple Kernel Learning, Object Detection, Sliding-Window, Support Vector Machine (SVM)

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