Superpixel-Based Adaptive Kernel Selection for Angular Effect Normalization of Remote Sensing Images With Kernel Learning

2017 ◽  
Vol 55 (8) ◽  
pp. 4262-4271 ◽  
Author(s):  
Yiqing Guo ◽  
Xiuping Jia ◽  
David Paull
Keyword(s):  
Remote Sensing ◽  
Kernel Learning ◽  
Remote Sensing Images ◽  
Kernel Selection ◽  
Adaptive Kernel ◽  
Selection For

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

2012 ◽  
Vol 532-533 ◽  
pp. 1258-1262
Author(s):  
Xiang Juan Li ◽  
Hao Sun ◽  
Xin Wei Zheng ◽  
Xian Sun ◽  
Hong Qi Wang
Keyword(s):  
Remote Sensing ◽  
Multiple Kernel Learning ◽  
Sliding Window ◽  
Google Earth ◽  
Kernel Learning ◽  
Remote Sensing Images ◽  
Multiple Objects ◽  
Multiple Kernel ◽  
Objects Detection ◽  
Kernel Svm

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.

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