Abstract: After reviewing the existing true ortho image rectification methods,we analyze image rectification and texture resampling processes that use a pixel-oriented strategy.These strategies generate inaccurate geometric features,imperfect texture structures of man-made objects.Thus,occlusion recovery and shadow compensation are difficult to automatically process,requiring much manual intervention.Aiming to solve these problems,this paper proposes a novel object-oriented true-ortho rectification method,implementing a high-level object-oriented strategy instead of a low-level pixel-oriented strategy.There are three main steps:① definition of physical objects and image object containing geometric and semantic information.In 3-D physical space,all the objects in the horizontal plane of projection form a seamless surface comprised of a series of contiguous triangular facets,in this paper this surface is represented by Semantics constrained Triangulated Irregular Network(STIN).The physical objects are characterized by different aspects: property,outline,geometry and topological relation,extracted from 3-D city models and point clouds.The image objects are made up of several pixels with semantic descriptions for spectral information,2-D geometry and topological relations.They can be extracted by image segmentation,edge detection,and texture clustering.② establishment of a global hierarchical spatial index of image objects.After deriving visual correspondence relationship between 3-D physical objects and 2-D image objects,we use the index to organize all the information from physical and image objects efficiently and provide a foundation for high performance true-ortho rectification and optimized image sampling.③ object-oriented true-ortho rectification and optimal image resampling.True-ortho rectification is carried out based on the physical objects as STIN.And based on the semantic links in the global index,the image objects with semantic information on visibility,integrality,and radiometric features can be chosen for optimized sampling to adaptively handle occlusion and shadows.Experimental results revealed that the proposed method has the advantages of high accuracy,high efficiency and full automation.