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Proceedings Paper

Unsupervised change-detection based on convolutional-autoencoder feature extraction
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Paper 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.

Paper Details

Date Published: 7 October 2019
PDF: 8 pages
Proc. SPIE 11155, Image and Signal Processing for Remote Sensing XXV, 1115510 (7 October 2019); doi: 10.1117/12.2533812
Show Author Affiliations
Luca Bergamasco, Fondazione Bruno Kessler (Italy)
Univ. degli Studi di Trento (Italy)
Sudipan Saha, Fondazione Bruno Kessler (Italy)
Univ. degli Studi di Trento (Italy)
Francesca Bovolo, Fondazione Bruno Kessler (Italy)
Lorenzo Bruzzone, Univ. degli Studi di Trento (Italy)

Published in SPIE Proceedings Vol. 11155:
Image and Signal Processing for Remote Sensing XXV
Lorenzo Bruzzone; Francesca Bovolo, Editor(s)

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