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Abstract:The growing availability of high-resolution satellite imagery provides an opportunity for identifying road objects. Most studies associated with road detection are scene-related and also based on the digital number of each pixel. Because images can provide more details (including color, size, shape, and texture), object-based processing is more advantageous. Therefore, in this paper, to handle the existing uncertainty of satellite image pixel values, using type-2 fuzzy set theory in combination with object-based image analysis is proposed. Because the main challenges of the type-2 fuzzy set are parameter tuning and extensive computations, a hybrid genetic algorithm (GA) consisting of Pittsburgh and cooperative-competitive learning schemes is proposed to address these problems. The most prominent feature of our research in this work is to establish a comprehensive object-based type-2 fuzzy logic system that enables us to detect roads in high-resolution satellite images with no training data. The validation assessment of road detection results using the proposed framework for independent images demonstrates the capability and efficiency of our method in identifying road objects. For more evaluation, a type-1 fuzzy logic system with the same structure as type-2 is tuned. Evaluations show that type-1 fuzzy logic system quality in training is very similar to that of the proposed type-2 fuzzy OPEN ACCESS Remote Sens. 2015, 7 8272 framework. However, in general, its lower accuracy, as inferred by validation assessments, makes the type-1 fuzzy logic system significantly different from the proposed type-2.

The increasing availability of high resolution satellite images is an opportunity to detect urban objects such as roads. In order to increasing the precision a new image analysis using object-based approaches has been proposed. In this paper, designing steps of knowledge based of road detection has been presented. In this field, an important challenge is the use of knowledge for automatic road objects identification, and a major issue is the formalization and exploitation of this knowledge. At first, optimum features, including spectral, texture and structural features, are detected using a genetic algorithm with a k-nearest neighbor classifier. After that a rule based road detection strategy has been developed using prior knowledge and optimum features interpretation. The method is designed and validated by IKONOS images of the urban areas of Hobart, Kish and Shiraz. The validation results highlight the capacity of the proposed method to automatically identify road objects using the knowledge based proposed system.