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ABSTRACT:Object-based classification is a promising methodology. Unlike pixel-based techniques which only use the layer pixel values, the object-based techniques can also use shape and context information of a scene texture. These extra degrees of freedom provided by the objects will aid the identification of visible textures. In this article, we present a procedure for object-based automatic classification. Using this procedure, we made algorithms to classify the geological faults using remote sensing data and fission-tracks in microscopic images. We also show how the notion of the object makes the task easy to use the result of classification for varius studies.

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Against the background of nuclear safeguards applications using commercially available satellite imagery, procedures for wide-area monitoring of the Iranian nuclear fuel cycle are investigated. Specifically, object-oriented classification combined with statistical change detection is applied to high-resolution imagery. In this context, a feature recognition and analysis tool, called SEaTH, has been developed for automatic selection of optimal object class features for subsequent classification. The application of SEaTH is presented in a case study of the NFRPC Esfahan, Iran. The transferability of classification models is discussed regarding the necessity for automation of extensive monitoring tasks.

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