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Interpretation of remotely sensed images in a context of multisensor fusion using a multispecialist architecture

机译:使用多专家架构在多传感器融合的背景下解释遥感影像

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A scene interpretation system is presented in a multisensor fusion context the application of which involves the interpretation of remote sensed images. The authors discuss how multisensor fusion is achieved, and derive the modeling problems for objects, scene, and strategy. The proposed multispecialist architecture takes into account the knowledge about sensors, the multiple viewing notion (shot), and the uncertainty and imprecision of models and data modeled with possibility theory. Generic models of objects are represented by concepts independent of sensors. Three kinds of specialists are present in the architecture: generic specialists (scene and conflict), semantic object specialists, and low level specialists. A blackboard structure with a centralized control is used. The interpreted scene is implemented as a matrix of pointers allowing easy conflict detection and spatial context verification. An interpretation system for SAR/SPOT sensor images is described, and an example of a scene interpretation is shown.
机译:在多传感器融合环境中提出了一种场景解释系统,其应用涉及遥感图像的解释。作者讨论了如何实现多传感器融合,并得出对象,场景和策略的建模问题。所提出的多专家体系结构考虑了有关传感器,多视图概念(镜头)以及使用可能性理论建模的模型和数据的不确定性和不精确性的知识。对象的通用模型由独立于传感器的概念表示。体系结构中存在三种专家:通用专家(场景和冲突),语义对象专家和低级专家。使用具有集中控制的黑板结构。解释后的场景被实现为指针矩阵,从而可以轻松地检测冲突和验证空间上下文。描述了用于SAR / SPOT传感器图像的解释系统,并且示出了场景解释的示例。

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