CD-GAN: a robust fusion-based generative adversarial network for unsupervised remote sensing change detection with heterogeneous sensors
This addresses the problem of detecting changes in Earth observation images from different sensors for remote sensing applications, but it is incremental as it builds on existing fusion and adversarial frameworks.
The paper tackles unsupervised change detection for heterogeneous optical remote sensing images by proposing CD-GAN, a method that embeds a pre-trained fusion network into an adversarial framework. It demonstrates versatility and effectiveness compared to state-of-the-art methods, though no concrete numbers are provided.
In the context of Earth observation, change detection boils down to comparing images acquired at different times by sensors of possibly different spatial and/or spectral resolutions or different modalities (e.g., optical or radar). Even when considering only optical images, this task has proven to be challenging as soon as the sensors differ by their spatial and/or spectral resolutions. This paper proposes a novel unsupervised change detection method dedicated to images acquired by such so-called heterogeneous optical sensors. It capitalizes on recent advances which formulate the change detection task into a robust fusion framework. Adopting this formulation, the work reported in this paper shows that any off-the-shelf network trained beforehand to fuse optical images of different spatial and/or spectral resolutions can be easily complemented with a network of the same architecture and embedded into an adversarial framework to perform change detection. A comparison with state-of-the-art change detection methods demonstrates the versatility and the effectiveness of the proposed approach.