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Presentation + Paper
9 October 2018 Destroyed-buildings detection from VHR SAR images using deep features
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Quick identification of post-earthquake destroyed buildings is critical for disaster management. It can be performed in unsupervised way by comparing pre-disaster and post-disaster Very-High-Resolution (VHR) SAR images. Spatial context needs to be modeled for effective change detection (CD) in VHR SAR images as they are complex and characterized by spatial correlation among pixels. We propose a unsupervised context-sensitive method for CD in multi-temporal VHR SAR images using pre-trained Convolutional-Neural-Network (CNN) based feature extraction. The sub-optimal CNN, pre-trained on an aerial optical image dataset and further optimized for using on SAR images by tuning the batch normalization layer of the CNN, enables us to obtain multi-temporal deep features that are pixelwise compared to identify the changed pixels. Detected changed pixels are further analyzed based on the double bounce property of the buildings in SAR images to detect the pixels corresponding to destroyed buildings. Experimental results on a dataset made up of a pair of multi-temporal VHR SAR images acquired by COSMO-SkyMed constellation on the city of L’Aquila (Italy) demonstrates effectiveness of the proposed approach.
Conference Presentation
© (2018) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Sudipan Saha, Francesca Bovolo, and Lorenzo Bruzzone "Destroyed-buildings detection from VHR SAR images using deep features", Proc. SPIE 10789, Image and Signal Processing for Remote Sensing XXIV, 107890Z (9 October 2018);

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