Supervised fuzzy analysis of single- and multichannel SAR data

The paper proposes a new learning fuzzy classification for single and multichannel synthetic aperture radar (SAR) data. It consists of the fusion of a supervised learning fuzzy distribution estimator and an unsupervised learning fuzzy vector quantizer. The adaptive algorithm accommodates varying requirements and delivers classification results in near real time. In addition to the classification, the user gets the reliability of the classification. This knowledge can be used to fuse several sensor channels efficiently. Automatically, a rule base is developed to deliver the required information with the highest possible reliability. In the author's example, the channels of a full polarimetric SAR are used. However, the algorithm can be extended also to optic and infrared channels. The proposed fuzzy classification system forms one module of an adaptive remote-sensing system. A conceptual design of this system is given. System control relies on an expert knowledge base and allows automatic configuration of the system to the considered remote-sensing application. This will lead to an increased usefulness of remotely sensed data.

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