Background Subtraction Based on GAN and Domain Adaptation for VHR Optical Remote Sensing Videos

The application of deep learning techniques in background subtraction for VHR optical remote sensing videos holds the potential to facilitate multiple intelligent remote sensing processing tasks. However, existing methods on background subtraction for VHR optical remote sensing videos are still facing technical challenges. First, conventional CNN and other networks are limited by performance constraints. Second, existing background subtraction methods are mostly trained by natural videos due to the lack of VHR optical remote sensing video datasets. Third, VHR optical remote sensing videos have large scene sizes. In our article, we design a novel deep learning network via fully utilizing GAN and domain adaptation, which has the ability to measure and minimize the discrepancy between feature distributions of natural videos and VHR optical remote sensing videos so that the background subtraction performance for VHR optical remote sensing videos is improved significantly. Numerous experiments are conducted on the CDnet 2014 dataset and VHR optical remote sensing video dataset. Tremendous experiments demonstrate that our proposed method achieves an average FM of 0.8533, which reveals excellent performance on background subtraction.

Abstract licence: CC BY-NC-ND

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