光学遥感图像场景理解对地球环境观测等领域有重大应用价值。光学遥感图像中目标间严重遮挡和目标剧烈形变给常规的场景理解方法带来了巨大挑战。为了解决严重遮挡和剧烈形变问题，本项目开展基于分层与或图模型的光学遥感图像场景理解方法研究。针对遮挡问题，本项目构建分层与或图模型精确描述场景、场景目标及目标间关系，并融合场景中目标上下文、外观、几何、内部结构、概率等多种信息，实现对存在严重遮挡的场景的准确理解，并解释目标间遮挡顺序，预测被遮挡部分；针对形变问题，本项目构建双向推理算法和基于分层与或图模型的属性语法，重构出大量不同结构的场景，实现对存在目标剧烈形变的场景的精确理解。利用本项目方法分析统计城市光学遥感图像中的绿化面积，验证本项目方法的有效性。本项目研究将有助于促进基于光学遥感图像的场景理解方法在地球环境观测等领域中的应用。

Scene understanding based on optical remote sensing image is of great value to earth environmental observing and other applications. In optical remote sensing Image, serious occlusions among objects and large deformations of objects pose huge challenges to conventional scene understanding methods. To solve these problems, scene understanding based on hierarchical And-Or-Graph Model for optical remote sensing image will be studied in this proposal. To handle the occlusion problem, the hierarchical And-Or Graph (HAOG) model will be constructed. In the HAOG, a scene will be hierarchically decomposed into multiple super-pixels. In addition, the HAOG integrates context, appearance, geometry, structure, and probability information in the scene. Therefore, the proposed HAOG can accurately describe a scene, objects in the scene, and the relationships among objects. Moreover, the HAOG can predict the occluded object parts, interpret the occlusion ordering, and achieve accurate understanding for scene with object occlusions. To solve the deformation problem, a bidirectional inference with attribute grammar will be studied to parse scenes. Based on the HAOG, a small number of production rules in attribute grammar and bidirectional inference will be integrated to obtain a large number of scene configurations from scene images. Therefore, our method can adapt to the large deformation of objects in scene images. Our method will be utilized to analyze city's green area from optical remote sensing image for verifying effectiveness. The proposed work in this proposal is beneficial to promote the application of scene understanding for optical remote sensing image in earth environmental observing and other fields.