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Remote Sens. 2016, 8(6), 506; doi:10.3390/rs8060506

Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection

1,†
,
1,2,* and 3,4,†
1
The Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System Science, Tsinghua University, Beijing 100084, China
2
The Joint Center for Global Change Studies, Beijing 100875, China
3
Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR), Wessling 82234, Germany
4
Signal Processing in Earth Observation (SiPEO), Technical University of Munich (TUM), Munich 80333, Germany
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Petri Pellikka, Lars Eklundh, Heiko Balzter, Lars T. Waser and Prasad S. Thenkabail
Received: 3 March 2016 / Revised: 23 May 2016 / Accepted: 12 June 2016 / Published: 16 June 2016
(This article belongs to the Special Issue Monitoring of Land Changes)
View Full-Text   |   Download PDF [2864 KB, uploaded 16 June 2016]   |  

Abstract

When exploited in remote sensing analysis, a reliable change rule with transfer ability can detect changes accurately and be applied widely. However, in practice, the complexity of land cover changes makes it difficult to use only one change rule or change feature learned from a given multi-temporal dataset to detect any other new target images without applying other learning processes. In this study, we consider the design of an efficient change rule having transferability to detect both binary and multi-class changes. The proposed method relies on an improved Long Short-Term Memory (LSTM) model to acquire and record the change information of long-term sequence remote sensing data. In particular, a core memory cell is utilized to learn the change rule from the information concerning binary changes or multi-class changes. Three gates are utilized to control the input, output and update of the LSTM model for optimization. In addition, the learned rule can be applied to detect changes and transfer the change rule from one learned image to another new target multi-temporal image. In this study, binary experiments, transfer experiments and multi-class change experiments are exploited to demonstrate the superiority of our method. Three contributions of this work can be summarized as follows: (1) the proposed method can learn an effective change rule to provide reliable change information for multi-temporal images; (2) the learned change rule has good transferability for detecting changes in new target images without any extra learning process, and the new target images should have a multi-spectral distribution similar to that of the training images; and (3) to the authors’ best knowledge, this is the first time that deep learning in recurrent neural networks is exploited for change detection. In addition, under the framework of the proposed method, changes can be detected under both binary detection and multi-class change detection. View Full-Text
Keywords: change detection; LSTM model; transferability; multi-spectral image; recurrent neural network change detection; LSTM model; transferability; multi-spectral image; recurrent neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Lyu, H.; Lu, H.; Mou, L. Learning a Transferable Change Rule from a Recurrent Neural Network for Land Cover Change Detection. Remote Sens. 2016, 8, 506.

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