人类对近地空间探索利用日益加剧，迫切需要对空间碎片和航天器进行监视。针对空间平台和目标特点，研究其成像方式、清晰化处理和识别方法具有重要意义。受空间平台载荷和功耗等限制，改进硬件难以提高图像分辨率，提出时空压缩感知计算成像方法，突破物理成像在分辨率等方面的局限性；空间平台与目标高速运动产生图像模糊，结合空间图像全局与局部结构信息，提出基于局部结构的图像复原方法，提高图像细节恢复性能；充分利用空间目标图像局部纹理结构在时空和尺度上的相似性，建立多帧多尺度的局部纹理相似性描述模型，改善帧数不足时超分辨重建性能，更加符合空间应用需求；空间目标形态模式复杂且样本少，提出小样本和特征不可靠条件下的概率图模型学习和推理方法，提高对空间目标局部部件的识别能力；空间目标序列在高维特征空间中分布非稠密、特征非连续，提出相应的流形学习方法，实现精确分类识别。研究成果将对提高航天探测及空间监视能力具有重要作用。

With the increasing insterest in the exploration and utilization of space, it is urgent to surveille space debris and space shuttle. Research on imaging, resolution-enhancement and recognition, taking the characteristics of space platform and the space objects into consideration, are of great significance. Because it faces difficulty to increase the resolution of space image as space based camera is limited by size and power, a spatial-temporal compressive sampling based computational imaging method is proposed to break the physical limitation of the imaging system on resolution. Another degeneration of space image is the blur introduced by the relative high speed motion between platform and objects. In order to recover more details of image, a local structure based image restoration method which attempts to compose global and local structure information together is proposed. As there are similarities among local texture in spatial, temporal and scale domain, a multi-frame and multi-scale local texture descriptive model is utilized to improve the performance of super resolution under inadequate amount of frames. As the result, this method fits space applications better. Parameter learning and statistics inference approaches for graphic model under inadequate sample and feature uncertainty is proposed to improve the ability to recognize local components of space object, since the shape and pattern of space object is complicated and the amount of sample is small. As space objects sequence is not distributed dense in feature space and its feature is not continuous, a corresponding manifold learning methods is proposed to precisely classify and recognize space objects. This research can greatly improve the ability of space exploration and surveillance.