Change detection of SAR images based on supervised contractive autoencoders and fuzzy clustering

Jie Geng, Hongyu Wang, Jianchao Fan, Xiaorui Ma

科研成果: 书/报告/会议事项章节会议稿件同行评审

35 引用 (Scopus)

摘要

In this paper, supervised contractive autoencoders (SCAEs) combined with fuzzy c-means (FCM) clustering are developed for change detection of synthetic aperture radar (SAR) images, which aim to take advantage of deep neural networks to capture changed features. Given two original SAR images, Lee filter is used in preprocessing and the difference image (DI) is obtained by log ratio method. Then, FCM is adopted to analyse DI, which yields pseudo labels for guiding the training of SCAEs. Finally, SCAEs are developed to learn changed features from bitemporal images and DI, which can obtain discriminative features and improve detection accuracies. Experiments on three data demonstrate that the proposed method outperforms some related approaches.

源语言英语
主期刊名RSIP 2017 - International Workshop on Remote Sensing with Intelligent Processing, Proceedings
出版商Institute of Electrical and Electronics Engineers Inc.
ISBN(电子版)9781538619902
DOI
出版状态已出版 - 23 6月 2017
已对外发布
活动2017 International Workshop on Remote Sensing with Intelligent Processing, RSIP 2017 - Shanghai, 中国
期限: 19 5月 201721 5月 2017

出版系列

姓名RSIP 2017 - International Workshop on Remote Sensing with Intelligent Processing, Proceedings

会议

会议2017 International Workshop on Remote Sensing with Intelligent Processing, RSIP 2017
国家/地区中国
Shanghai
时期19/05/1721/05/17

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