遥感影像大范围地表信息缺失区域的修复理论与方法研究

项目名称： 遥感影像大范围地表信息缺失区域的修复理论与方法研究

项目编号： No.41271376

项目类型： 面上项目

立项/批准年度： 2013

项目学科： 天文学、地球科学

项目作者： 沈焕锋

作者单位： 武汉大学

项目金额： 75万元

中文摘要： 遥感影像经常受到大范围死像元、厚云的影响，大大限制了其在各领域的应用潜力。由于理论框架的局限性，现有的遥感影像修复方法远不能满足实际处理的需求。本项目拟引入数学、统计、最优化领域的最新理论，研究和发展遥感影像大范围地表信息缺失区域的修复理论与方法。充分利用Grouplet变换准确捕捉影像几何流的特点，发展无互补信息的遥感影像修复方法；充分利用压缩感知理论对非线性问题与高维信号处理的优势，建立基于谱段互补信息的影像修复方法；基于非局部正则化约束模型建立变分求解框架，发展基于时相互补信息的影像修复方法；结合压缩感知与非局部正则化修复模型，实现集成时-谱互补信息的影像修复。本项目研究和发展遥感影像信息处理的新理论与新方法，可以大幅提升遥感数据的应用潜力，具有重要的理论与应用意义。

中文关键词： 遥感影像；信息重建；信息修复；去云；死像元

英文摘要： Remotely sensed images are often degraded by large areas of dead pixels or thick clouds, which greatly weaken the potential applications in various fields. Due to the limitations of the traditional technique frameworks, the existing inpainting methods for remote sensing images can not satisfy the practical requirements. This project is to research and develop robust inpainting methods for remote sensing images by adopting the latest theory in mathematics, statistics and optimization fields. Firstly, an inpainting method for remote sensing images without complementary information is developed using Grouplet transform which is able to accurately capture geometry flow of the images. Secondly, an inpainting method for remote sensing images with complementary spectrum information is developed using compressed sensing theory which has been validated to be excellent in high-dimensional signal processing. Thirdly, an inpainting method for remote sensing images with time complementary information is developed using non-local regularization constraint model. Lastly, an inpainting method with integration of temporal-spectral complementary information is developed by combining compressed sensing with non-local regularization model. This study can expand new theories and produce new methods for remote sensing image processin

英文关键词： remote sensing image；information reconstruction；inpainting；cloud removal；dead pixel