针对空间信息网络中空间信息稀疏表征与融合处理关键科学问题取得的理论方法成果，基于已部署集成项目构建的验证平台，本项目重点开展空间信息网络环境下的遥感影像稀疏表征与融合处理综合验证研究。本项目采用分阶段、分层次的星地协同验证思路，具体研究如下内容：（1）轻小型星相机超高精度姿态确定验证；（2）基于深度稀疏表征的智能目标检测验证；（3）基于稀疏计算的多时相影像变化信息提取验证；（4）基于稀疏表征的影像高倍智能压缩验证；（5）基于典型的应用场景的综合集成试验验证。基于上述研究，解决高精度在轨实时处理、目标和变化信息的智能提取以及面向任务的高倍率智能压缩等瓶颈难题，为空间信息网络有限带宽资源条件下，海量遥感影像“快”、“准”、“灵”的高精度智能实时终端应用服务提供支撑。

Based on the verification platform of deployed integrated projects and theoretical and technical achievements on the key scientific problem of sparse representation and fusion processing in spatial information net, this project focuses on the comprehensive verification of sparse representation and fusion processing of remote sensing images in the space information network environment. According to the idea of "satellite-to-ground collaborative verification" at different stages and levels, the following contents are studied concretely: (1) verification of ultra-high precision attitude determination for small and light small star camera, (2) verification of intelligent target detection based on deep sparse representation, (3) verification of multi-temporal image change information extraction based on sparse computing, (4) verification of high-ratio intelligent compression based on image sparse representation, (5) integrated demonstration based on typical application scenarios. The aim of the project is to solve the bottleneck problems of high accuracy real-time processing on-board, intelligent extraction of target and change information, and task oriented high-ratio intelligent compression, so that it can provide support for the high precision intelligent real-time terminal application service of "fast", "precise " and " smart " for the massive remote sensing images under the limited bandwidth of space information network.