Object-based change detection from satellite imagery by segmentation optimization and multi-features fusion

This article presents a novel object-based change detection (OBCD) approach in high-resolution remote-sensing images by means of combining segmentation optimization and multi-features fusion. In the segmentation optimization, objects with optimized boundaries and proper sizes are generated by object...

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Published inInternational journal of remote sensing Vol. 38; no. 13; pp. 3886 - 3905
Main Authors Peng, Daifeng, Zhang, Yongjun
Format Journal Article
LanguageEnglish
Published London Taylor & Francis 03.07.2017
Taylor & Francis Ltd
Subjects
algorithms
Change detection
data collection
Detection
Feature extraction
High resolution
Image detection
Image resolution
Image segmentation
Imagery
Information retrieval
Mathematical models
Object recognition
Optimization
Qualitative analysis
quantitative analysis
Remote sensing
Resolution
Satellite imagery
Satellites
Spaceborne remote sensing
Online AccessGet full text
ISSN0143-1161
1366-5901
1366-5901
DOI10.1080/01431161.2017.1308033

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Abstract This article presents a novel object-based change detection (OBCD) approach in high-resolution remote-sensing images by means of combining segmentation optimization and multi-features fusion. In the segmentation optimization, objects with optimized boundaries and proper sizes are generated by object intersection and merging (OIM) processes, which ensures the accurate information extraction from image objects. Within multi-features fusion and change analysis, the Dempster and Shafer (D-S) evidence theory and the Expectation-Maximization (EM) algorithm are implemented, which effectively utilize multidimensional features besides avoiding the selection of an appropriate change threshold. The main advantages of our proposed method lie in the improvement of object boundary and the fuzzy fusion of multi-features information. The proposed approach is evaluated using two different high-resolution remote-sensing data sets, and the qualitative and quantitative analyses of the results demonstrate the effectiveness of the proposed approach.
AbstractList This article presents a novel object-based change detection (OBCD) approach in high-resolution remote-sensing images by means of combining segmentation optimization and multi-features fusion. In the segmentation optimization, objects with optimized boundaries and proper sizes are generated by object intersection and merging (OIM) processes, which ensures the accurate information extraction from image objects. Within multi-features fusion and change analysis, the Dempster and Shafer (D-S) evidence theory and the Expectation-Maximization (EM) algorithm are implemented, which effectively utilize multidimensional features besides avoiding the selection of an appropriate change threshold. The main advantages of our proposed method lie in the improvement of object boundary and the fuzzy fusion of multi-features information. The proposed approach is evaluated using two different high-resolution remote-sensing data sets, and the qualitative and quantitative analyses of the results demonstrate the effectiveness of the proposed approach.
Author Zhang, Yongjun
Peng, Daifeng
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  surname: Zhang
  fullname: Zhang, Yongjun
  email: zhangyj@whu.edu.cn
  organization: School of Remote Sensing and Information Engineering, Wuhan University
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Snippet This article presents a novel object-based change detection (OBCD) approach in high-resolution remote-sensing images by means of combining segmentation...
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SubjectTerms algorithms
Change detection
data collection
Detection
Feature extraction
High resolution
Image detection
Image resolution
Image segmentation
Imagery
Information retrieval
Mathematical models
Object recognition
Optimization
Qualitative analysis
quantitative analysis
Remote sensing
Resolution
Satellite imagery
Satellites
Spaceborne remote sensing
Title Object-based change detection from satellite imagery by segmentation optimization and multi-features fusion
URI https://www.tandfonline.com/doi/abs/10.1080/01431161.2017.1308033
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