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

我们的目的是通过灾前SAR-光学图像对和灾后SAR图像的输入，预测具有滑坡细节的灾后光学图像。以往基于深度学习的方法可以恢复视觉效果良好的光学图像，可惜随着山体滑坡的消失而消失。为了重建物理上有意义的细节，我计算了灾后SAR图像的权重矩阵来衡量每个像素的重要性，并利用该权重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，并在数据集的基础上开发了多时相SARoptical方法。在相关工作的基础上，我们还开发了几种用于遥感图像去噪、复原和重建的图像质量改善方法。首先，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，我们尝试通过计算相机将彩色图像和测量数据重建高光谱图像。发表论文包括：Pattern Recognition期刊收录论文1篇，IEEE transactions on image processing期刊收录论文1篇。

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.