基于贝叶斯统计的遥感影像在轨特征提取与压缩传输

项目名称： 基于贝叶斯统计的遥感影像在轨特征提取与压缩传输

项目编号： No.91538107

项目类型： 重大研究计划

立项/批准年度： 2016

项目学科： 无线电电子学、电信技术

项目作者： 陶晓明

作者单位： 清华大学

项目金额： 84万元

中文摘要： 近年来，随着观测带宽、量化精度以及分辨率的不断提升，空间遥感影像数据量成几何级数增长。受限于信源的信息熵，现有以JPEG2000、CCSDS为代表的通用压缩方法，在近无损要求下仅能获得4倍左右的压缩率，很难进一步提升。受HEVC等视频压缩方法的启发，充分利用前后两帧图像的强相关性，只对少量变化信息进行编码，可极大地提升编码性能。然而，两次拍摄的空间遥感图像之间的相关性并不体现为直接的线性关系，而是呈现长程相关、复杂耦合的非线性关系，其互信息的挖掘与利用面临基础性的挑战。本项目瞄准未来空间信息网络国际研究前沿，将先验样本引入海量遥感图像处理，重点突破遥感影像先验信息的融合与表征、遥感影像稳定局部特征的描述与在轨提取、以及基于贝叶斯统计学习的遥感影像压缩编码等技术难题，大幅降低空间网络信息传输的数据量，为有效支撑海量高时效性空间信息获取与利用等国家重大需求的发展奠定理论基础。

中文关键词： 特征提取；压缩编码；贝叶斯统计学习；遥感图像处理

英文摘要： As a result of the continued increase of observational bandwidth, quantization precision, and imaging resolution, the volume of data obtained from space-based remote sensing has shown geometric growth. The performance of current generic compression methods commonly used for remote sensing images, including JPEG2000 and CCSDS, are bounded below by the information entropy of the original images; thus they typically can only achieve a 4:1 compression ratio. Inspired by video compression methods such as HEVC, we note that the strong temporal correlation between consecutive frames can be used to greatly improve the coding performance by only coding frame differences and motion vectors. However, even between two captured images at the same geological location, their correlation cannot be expressed by a simple linear relationship; rather, it is a non-linear relationship exhiibiting long-range correlation and complex coupling, making the utilization of their inherit correlation a fundamental challenge. Focusing on the state-of-the-art of international future space information network research, this project pioneers the use of a priori samples in large-volume remote sensing image processing, particularly aiming at break-throughs in the integration and representation of prior information in remote sensing images, the in-orbit description and extraction of invariant local features off remote sensing images, and remote sensing image compression and coding based on Bayesian statistical learning, thereby greatly reducing the data transfer volume of space information networks, and providing a theoretical foundation for supporting key national needs in large-volume low-delay space-based information acquisition and utilization.

英文关键词： Feature Extraction;Compression Coding;Bayesian Statistical Learning;Remote Sensing Image Processing