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7 October 2019 Unsupervised change-detection based on convolutional-autoencoder feature extraction
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Abstract
Rapid identification of areas affected by changes is a challenging task in many remote sensing applications. Sentinel-1 (S1) images provided by the European Space Agency (ESA) can be used to monitor such situations due to its high temporal and spatial resolution and indifference to weather. Though a number of deep learning based methods have been proposed in the literature for change detection (CD) in multi-temporal SAR images, most of them require labeled training data. Collecting sufficient labeled multi-temporal data is not trivial, however S1 provides abundant unlabeled data. To this end, we propose a solution for CD in multi-temporal S1 images based on unsupervised training of deep neural networks (DNNs). Unlabeled single-time image patches are used to train a multilayer convolutional-autoencoder (CAE) in unsupervised fashion by minimizing the reconstruction error between the reconstructed output and the input. The trained multilayer CAE is used to extract multi-scale features from both the pre and post change images that are analyzed for CD. The multi-scale features are fused according to a detail-preserving scale-driven approach that allows us to generate change maps by preserving details. The experiments conducted on a S1 dataset from Brumadinho, Brazil confirms the effectiveness of the proposed method.
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© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Luca Bergamasco, Sudipan Saha, Francesca Bovolo, and Lorenzo Bruzzone "Unsupervised change-detection based on convolutional-autoencoder feature extraction", Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115510 (7 October 2019); https://doi.org/10.1117/12.2533812
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