A Novel Multiple Kernel Learning Framework for Remote Sensing Scene Classification

Author(s):  
Xiaoyong Bian ◽  
Yuxia Sheng ◽  
Yan Xu ◽  
Qian Du
2012 ◽  
Vol 532-533 ◽  
pp. 1258-1262
Author(s):  
Xiang Juan Li ◽  
Hao Sun ◽  
Xin Wei Zheng ◽  
Xian Sun ◽  
Hong Qi Wang

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.


2018 ◽  
Vol 56 (3) ◽  
pp. 1425-1443 ◽  
Author(s):  
Saeid Niazmardi ◽  
Begum Demir ◽  
Lorenzo Bruzzone ◽  
Abdolreza Safari ◽  
Saeid Homayouni

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