|Table of Contents|

 Automatic Change Detection of High Resolution Remote 
Sensing Images Based on Level Set Evolution 
and Support Vector Machine Classification

(PDF)

《哈尔滨理工大学学报》[ISSN:1007-2683/CN:23-1404/N]

Issue:
2019年01期
Page:
78-84
Research Field:
计算机与控制工程
Publishing date:

Info

Title:
 Automatic Change Detection of High Resolution Remote 
Sensing Images Based on Level Set Evolution 
and Support Vector Machine Classification

Author(s):
 YAN MingCAO GuoXIA Meng
 (School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China)
Keywords:
 Keywords:change detection level set evolution support vector machine (SVM) multiresolution analysis image segmentation
PACS:
TP752
DOI:
10.15938/j.jhust.2019.01.013
Abstract:
 Abstract:We propose a method for change detection in highresolution remote sensing images by means of level set evolution and Support Vector Machine (SVM) classification, which combined both pixellevel method and objectlevel method Both pixelbased change features and objectbased ones are extracted to improve the discriminability between the changed class and the unchanged classAt the pixellevel, the change detection problem is formulated as a segmentation issue using level set evolution in the difference image At the objectlevel, potential training samples are selectedfrom the segmentation results without manual intervention into SVM classifier Thereafter, the final changes are obtained by combining the pixelbased changes and the objectbased changes A chief advantage of our approach is being able to select appropriate samples for SVM classifier training Furthermore, our proposed method helps improving the accuracy and the degree of automation We systematically evaluated it with a variety of SPOT5 images and aerial images Experimental results demonstrated the accuracy of our proposed method

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Last Update: 2019-03-26