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

我们的目的是预测灾后的光学图像与滑坡细节，从灾前SAR光学图像对和灾后SAR图像的输入。以前基于深度学习的方法可以恢复视觉效果良好的光学图像，不幸的是随着山体滑坡的消失而消失。为了重建具有物理意义的细节，计算灾后SAR图像的权值矩阵来衡量每个像素的重要性，并利用权值矩阵来指导光学图像的重建。到目前为止，我已经完成了数据集的准备工作，并在此基础上发展了多时相SAR光学方法。 在相关工作的基础上，我们还发展了几种遥感图像去噪、分辨率和重建的图像质量改善方法。首先，我们尝试从低空间分辨率的高光谱图像和低光谱分辨率的多光谱图像重建高光谱图像。其次，我们尝试从彩色图像和测量通过计算相机的高光谱图像重建。相关论文包括1篇被模式识别杂志录用，1篇被IEEE图像处理杂志录用。

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.