大场景高分辨SAR影像在语义空间中的区域图的作用下，被划分为混合集聚结构地物像素子空间、结构地物像素子空间和匀质地物像素子空间。针对大场景高分辨率SAR影像存在获取类别标签样本数据难的问题，通过对三个不同结构地物的像素子空间的无监督分割来解决SAR影像的无监督分割。由于极不匀质区域地物表示的复杂性与该区域目标大小和形状的多样性、相邻目标空间拓扑结构的多样性、目标散射特性和相干斑噪声等有密切的关系，与匀质区域和不匀质区域地物表示的复杂性相比，极不匀质区域地物表示的难度更大，导致混合集聚结构地物像素子空间的无监督分割更具有挑战性；因此，本项目拟重点研究极不匀质区域的无监督层次表征学习模型和方法来实现混合集聚结构地物像素子空间的无监督分割，为无监督表征学习探索出一条非深层且可解释的新的研究途径。

Under the application of the regional map in the semantic space, the large scene and high-resolution SAR images are divided into hybrid aggregated, structural and homogeneous pixel-level subspaces. As it is difficult to obtain the labeled samples for the large scene and high-resolution SAR images, the unsupervised segmentation problem of the large scene and high-resolution SAR images is solved by the unsupervised segmentation of three structure pixel-level subspaces. The complexity of the object representation of the extremely unhomogeneous pixel-level subspace has a close relationship with the diversity of the size and shape of the target in the region, the diversity of spatial topological structures with adjacent targets, the target scattering characteristics and the speckle noise, etc. So compared with the homogeneous and unhomogeneous region, the extremely unhomogeneous region is more difficult to represent, which leads to the unsupervised segmentation of the hybird aggreated structure pixel-level subspace more challenging. Therefore, this project will focus on the unsupervised hierarchical represention learning model and method to realize the unsupervised segemention of the hybird aggregated stucture pixel-level subspace, and explore a new non-deep and interpretable apporach to realize unsupervised representation learning.