Progress in automatic analysis of multi-temporal remotely-sensed data

Abstract The increasing volume of remotely-sensed data has led to a need for efficient automatic analysis procedures. A system is presented for automatic, knowledge-based segmentation of remotely-sensed images of the land. The system uses the information in time sequences of remotely-sensed data together with cartographic map data and domain expertise to build a model of the scene in terms of segments and their possible classes. The accuracies of segmentation and subsequent classification are shown to be superior to traditional automatic techniques. In addition, potential changes in the scene are isolated.

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