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Open AccessArticle

Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning

by Xin Wang 1,2,3, Sicong Liu 4, Peijun Du 1,2,3,*, Hao Liang 1,2,3, Junshi Xia 5 and Yunfeng Li 1,2,3
1
Key Laboratory for Satellite Mapping Technology and Applications of National Administration of Surveying, Mapping and Geoinformation of China, Nanjing University, Nanjing 210023, China
2
Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing 210023, China
3
Collaborative Innovation Center of South China Sea Studies, Nanjing 210093, China
4
College of Surveying and Geoinformatics, Tongji University, Shanghai 200092, China
5
Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo 113-8654, Japan
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(2), 276; https://doi.org/10.3390/rs10020276
Received: 6 December 2017 / Revised: 27 January 2018 / Accepted: 8 February 2018 / Published: 11 February 2018
(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)
To improve the accuracy of change detection in urban areas using bi-temporal high-resolution remote sensing images, a novel object-based change detection scheme combining multiple features and ensemble learning is proposed in this paper. Image segmentation is conducted to determine the objects in bi-temporal images separately. Subsequently, three kinds of object features, i.e., spectral, shape and texture, are extracted. Using the image differencing process, a difference image is generated and used as the input for nonlinear supervised classifiers, including k-nearest neighbor, support vector machine, extreme learning machine and random forest. Finally, the results of multiple classifiers are integrated using an ensemble rule called weighted voting to generate the final change detection result. Experimental results of two pairs of real high-resolution remote sensing datasets demonstrate that the proposed approach outperforms the traditional methods in terms of overall accuracy and generates change detection maps with a higher number of homogeneous regions in urban areas. Moreover, the influences of segmentation scale and the feature selection strategy on the change detection performance are also analyzed and discussed. View Full-Text
Keywords: object-based change detection (OBCD); segmentation; scale; multiple features; supervised classifier; ensemble learning (EL) object-based change detection (OBCD); segmentation; scale; multiple features; supervised classifier; ensemble learning (EL)
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Wang, X.; Liu, S.; Du, P.; Liang, H.; Xia, J.; Li, Y. Object-Based Change Detection in Urban Areas from High Spatial Resolution Images Based on Multiple Features and Ensemble Learning. Remote Sens. 2018, 10, 276.

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