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A Change Detection Method for Remote Sensing Image Based on Multi-feature Differencing Kernel Svm : Volume I-7, Issue 1 (17/07/2012)

By Lin, Y.

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Book Id: WPLBN0004013696
Format Type: PDF Article :
File Size: Pages 9
Reproduction Date: 2015

Title: A Change Detection Method for Remote Sensing Image Based on Multi-feature Differencing Kernel Svm : Volume I-7, Issue 1 (17/07/2012)  
Author: Lin, Y.
Volume: Vol. I-7, Issue 1
Language: English
Subject: Science, Isprs, Annals
Collections: Periodicals: Journal and Magazine Collection, Copernicus GmbH
Historic
Publication Date:
2012
Publisher: Copernicus Gmbh, Göttingen, Germany
Member Page: Copernicus Publications

Citation

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Lin, Y., Liu, B., Lu, Y., Pan, C., & Lv, Q. (2012). A Change Detection Method for Remote Sensing Image Based on Multi-feature Differencing Kernel Svm : Volume I-7, Issue 1 (17/07/2012). Retrieved from http://kindle.worldlibrary.net/


Description
Description: Research Center of Remote Sensing and Spatial Information Technology, Tongji University, Shanghai, 200092, China. Based on the support vector machine (SVM) tools and multiple kernel method, the combinations of kernel functions were mainly discussed. The construction method of image differencing kernel with multi-feature (spectral feature and textural feature) has been developed. Through this method and weighting of the categories' samples, the improved SVM change detection model has been proposed, which could realize the direct extraction of spatial distribution information from several change classes. From the experiments we can draw the following conclusions: with the help of multiple kernel function integrating spectral features and texture information, the new change detection model can achieve higher detection accuracy than the traditional methods and is suitable for the small-sample experiment. Furthermore, it avoids the complex and uncertainty in determining change threshold required in the old detection methods.

Summary
A CHANGE DETECTION METHOD FOR REMOTE SENSING IMAGE BASED ON MULTI-FEATURE DIFFERENCING KERNEL SVM

 

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