Subir Paul, Laura Elena Cue la Rosa, Pedram Ghamisi, and Richard Gloaguen

Satellite images are widely used for change detection in various applications, like, agricultural and forest cover area monitoring and management, urban planning, disaster management etc. which require prior information about the timeline of changes. However, it is very challenging to identify precise timeline of changes and to acquire corresponding labelled reference of changes. Considering this, Convolutional Auto-Encoder (CAE)-based approaches has been proposed for unsupervised change detection from bi-temporal remote sensing images. However, these type of approaches require prior knowledge about the exact moment the change occurs in order to employ the bi-temporal images, one pre-change and one post-change. This work introduces the utilization of satellite image time-series (SITS) data in a Joint 3D CAE-based unsupervised approach to detect annual changes. Our study focuses on a mining area where the changes mostly occurs due to tailings, waste and ore deposit, mining pit, mining residue, and so on. Here, we have explored Synthetic Aperture Radar (SAR) (i.e. Sentinel-1) data along with optical (i.e. Sentinel-2) data, since optical data alone was not able to detect all the changes. The recall of detected change areas during 2016–2018 has improved by about 16% with the use of combination of Sentinel-1&2 data as compared to utilization of only Sentinel-2 images.

International Journal of Remote Sensing, 44, 5, 2023-03-22.

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IARAI Authors
Dr. Pedram Ghamisi
Remote Sensing
Change Detection, Convolutional Autoencoder, Deep Learning, Remote Sensing, Satellite Images


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