Generative Adversarial Networks Based Multi-Sensor Remote Sensing Image Translation for Disaster Damage Mapping

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Our purpose is to predict the post-disaster optical image with landslide details, from the input of pre-disaster SAR-optical image pairs and post-disaster SAR image. Previous deep learning based methods can recover the optical image in good visual, unfortunately with the landslides disappeared. To reconstruct physically meaningful details, I calculate a weight matrix of post-disaster SAR image to measure the importance of each pixel, and utilize the weight matrix to guild the reconstruction of optical image. Until now, I have finished the dataset preparation, and on the basis of the dataset, I developped the multi-temporal SARoptical method. On the basis of the related works, we also developped several image quality improvement methods for remote sensing image denoising, resotration, and reconstruction. Firstly, we try to reconstruct the hypersepctral image from the low-spatial-resolution hyperspectral image and low-spectral-resolution multispectral image. Secondly, we try to reconstruct the hyperspectral image from color image and the measurements via computational camera. The related publications include 1 paper accepted by Pattern Recognition, 1 paper accpepted by IEEE transactions on image processing.