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Open AccessArticle

Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods

by Lei Ma 1,2,3,4, Manchun Li 1,3,4,*, Thomas Blaschke 2, Xiaoxue Ma 4, Dirk Tiede 2, Liang Cheng 1,3,4, Zhenjie Chen 1,3,4 and Dong Chen 1,4
1
Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China
2
Department of Geoinformatics—Z_GIS, University of Salzburg, Hellbrunner Str. 34, Salzburg A-5020, Austria
3
Collaborative Innovation Center for the South Sea Studies, Nanjing University, Nanjing 210023, China
4
School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210023, China
*
Author to whom correspondence should be addressed.
Academic Editors: Chandra Giri, James Campbell, Xiaofeng Li and Prasad S. Thenkabail
Remote Sens. 2016, 8(9), 761; https://doi.org/10.3390/rs8090761
Received: 11 May 2016 / Revised: 31 August 2016 / Accepted: 9 September 2016 / Published: 16 September 2016
(This article belongs to the Special Issue Monitoring of Land Changes)
Object-based change detection (OBCD) has recently been receiving increasing attention as a result of rapid improvements in the resolution of remote sensing data. However, some OBCD issues relating to the segmentation of high-resolution images remain to be explored. For example, segmentation units derived using different segmentation strategies, segmentation scales, feature space, and change detection methods have rarely been assessed. In this study, we have tested four common unsupervised change detection methods using different segmentation strategies and a series of segmentation scale parameters on two WorldView-2 images of urban areas. We have also evaluated the effect of adding extra textural and Normalized Difference Vegetation Index (NDVI) information instead of using only spectral information. Our results indicated that change detection methods performed better at a medium scale than at a fine scale where close to the pixel size. Multivariate Alteration Detection (MAD) always outperformed the other methods tested, at the same confidence level. The overall accuracy appeared to benefit from using a two-date segmentation strategy rather than single-date segmentation. Adding textural and NDVI information appeared to reduce detection accuracy, but the magnitude of this reduction was not consistent across the different unsupervised methods and segmentation strategies. We conclude that a two-date segmentation strategy is useful for change detection in high-resolution imagery, but that the optimization of thresholds is critical for unsupervised change detection methods. Advanced methods need be explored that can take advantage of additional textural or other parameters. View Full-Text
Keywords: multiresolution segmentation; WorldView-2; MAD; two-date change detection; OBCD; high spatial resolution; sensitivity; specificity multiresolution segmentation; WorldView-2; MAD; two-date change detection; OBCD; high spatial resolution; sensitivity; specificity
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Ma, L.; Li, M.; Blaschke, T.; Ma, X.; Tiede, D.; Cheng, L.; Chen, Z.; Chen, D. Object-Based Change Detection in Urban Areas: The Effects of Segmentation Strategy, Scale, and Feature Space on Unsupervised Methods. Remote Sens. 2016, 8, 761.

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