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Remote Sensing
Volume 11
Issue 23
10.3390/rs11232787
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Article

Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model

by
Xiaokang Zhang
1,2,
Wenzhong Shi
1,*,
Zhiyong Lv
3 and
Feifei Peng
4
1
Department of Land Survey and Geo-Informatics, The Hong Kong Polytechnic University, Hong Kong, China
2
Hubei Soil and Water Conservation Engineering Research Center, Hubei Water Resources Research Institute, Wuhan 430070, China
3
School of Computer Science and Engineering, Xi’An University of Technology, Xi’an 710048, China
4
College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(23), 2787; https://doi.org/10.3390/rs11232787
Submission received: 6 November 2019 / Revised: 22 November 2019 / Accepted: 23 November 2019 / Published: 26 November 2019
(This article belongs to the Special Issue Lightweight Deep Neural Networks for Remote Sensing Image Understanding)
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Abstract

This paper presents a novel approach for automatically detecting land cover changes from multitemporal high-resolution remote sensing images in the deep feature space. This is accomplished by using multitemporal deep feature collaborative learning and a semi-supervised Chan–Vese (SCV) model. The multitemporal deep feature collaborative learning model is developed to obtain the multitemporal deep feature representations in the same high-level feature space and to improve the separability between changed and unchanged patterns. The deep difference feature map at the object-level is then extracted through a feature similarity measure. Based on the deep difference feature map, the SCV model is proposed to detect changes in which labeled patterns automatically derived from uncertainty analysis are integrated into the energy functional to efficiently drive the contour towards accurate boundaries of changed objects. The experimental results obtained on the four data sets acquired by different high-resolution sensors corroborate the effectiveness of the proposed approach.
Keywords: change detection; deep feature learning; Chan–Vese model; high-resolution remote sensing imagery; semi-supervised learning; uncertainty analysis change detection; deep feature learning; Chan–Vese model; high-resolution remote sensing imagery; semi-supervised learning; uncertainty analysis
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MDPI and ACS Style

Zhang, X.; Shi, W.; Lv, Z.; Peng, F. Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model. Remote Sens. 2019, 11, 2787. https://doi.org/10.3390/rs11232787

AMA Style

Zhang X, Shi W, Lv Z, Peng F. Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model. Remote Sensing. 2019; 11(23):2787. https://doi.org/10.3390/rs11232787

Chicago/Turabian Style

Zhang, Xiaokang, Wenzhong Shi, Zhiyong Lv, and Feifei Peng. 2019. "Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model" Remote Sensing 11, no. 23: 2787. https://doi.org/10.3390/rs11232787

APA Style

Zhang, X., Shi, W., Lv, Z., & Peng, F. (2019). Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model. Remote Sensing, 11(23), 2787. https://doi.org/10.3390/rs11232787

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