高分辨率遥感影像中地面细节进一步凸显，地物内部的成像差异在后续的图像分割中呈现为具有光谱差异性的多个图斑，图斑间的组合推理成为认知海岸带地物类型的新挑战。本项申请提出基于语义网络和面向对象的海岸带高分辨率遥感影像中地物成像差异的识别机制和实现途径。通过应用海岸带典型地物的地表场景模型，构建图像对象层次的空间语义网络表达成像差异及其在图像上的光谱特征；待解译的遥感图像通过多尺度分析、图像对象识别、遥感特征提取，建立邻域空间关系，形成内含空间信息的图组织图像对象及其遥感特征；利用空间关系和光谱特征约束的图匹配算法，图像对象邻域关系图与空间语义网络间建立映射，以此识别地物对象的成像差异信息。本项研究试图在米级遥感影像解译中应用场景成像分析认知海岸带地物的成像差异，在遥感信息获取中引入可计算分析的知识表达和利用方式，探索遥感图像新的认知模式与机器理解机制。

More details of earth surface appeared in the high resolution remotely sensed imagery. The imaging differences between the parts of ground object result in several separate segments during the later multi-scale analysis. The reasoning and combination of neighbour segments become a challenge of image understanding in coastal remote sensing. The proposed project utilises semantic network and image object oriented approach for spatial imaging context information extraction in high resolution satellite imagery of coastal zone. The scene model is applied for imaging procedure analysis, which provide the imaging differences of the parts of special object and validated in the typical high resolution images. To express spatial knowledge, spatial semantic network model takes the images as the basic analysis object and semantic surrounding, analyzes its semantic feature of the space, attribute, and context information. The handling image is performed multi-scale analysis to yield hierarchic candidates firstly. And the meaningful image objects are identified and the feature extracted from them. The neighborshood among them are calculated by using Voronoi algorithm. Then the object-oriented graph forms which present the whole spatial location and topological relation. A novel spatial structure match algorithm is used to get a spanning tree between the image-object graph and semantic network, which constraint by the paths that have max spatial relationship regularity. The mapping result is used to identify the corresponding segments of the imaging parts of object finally. Hereby the scene imaging model is used to step over the difficult in the interpretation of coastal remote sensing images. The philosophy, methodology and related algorithms of this model are fully described in terms of basic ideas, model definitions, dynamic mechanisms, reasoning methods and implementation abilities. The proposed project can helped to improve cognitive model and machine intelligence in remote sensing.