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Remote Sens. 2019, 11(2), 142;

Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images

Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, International Research Center for Intelligent Perception and Computation, Joint International Research Laboratory of Intelligent Perception and Computation, School of Artificial Intelligence, Xidian University, Xi’an 710071, China
School of Computer Science and Technology, Xidian University, Xi’an 710071, China
Author to whom correspondence should be addressed.
Received: 5 November 2018 / Revised: 23 December 2018 / Accepted: 25 December 2018 / Published: 12 January 2019
PDF [4744 KB, uploaded 12 January 2019]   |  


In this paper, a novel change detection approach based on multi-grained cascade forest
(gcForest) and multi-scale fusion for synthetic aperture radar (SAR) images is proposed. It detects
the changed and unchanged areas of the images by using the well-trained gcForest. Most existing
change detection methods need to select the appropriate size of the image block. However, the
single size image block only provides a part of the local information, and gcForest cannot achieve a
good effect on the image representation learning ability. Therefore, the proposed approach chooses
different sizes of image blocks as the input of gcForest, which can learn more image characteristics
and reduce the influence of the local information of the image on the classification result as well.
In addition, in order to improve the detection accuracy of those pixels whose gray value changes
abruptly, the proposed approach combines gradient information of the difference image with the
probability map obtained from the well-trained gcForest. Therefore, the image edge information can
be enhanced and the accuracy of edge detection can be improved by extracting the image gradient
information. Experiments on four data sets indicate that the proposed approach outperforms other
state-of-the-art algorithms.

Graphical abstract

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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Ma, W.; Yang, H.; Wu, Y.; Xiong, Y.; Hu, T.; Jiao, L.; Hou, B. Change Detection Based on Multi-Grained Cascade Forest and Multi-Scale Fusion for SAR Images. Remote Sens. 2019, 11, 142.

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