A real-time urban area detection algorithm based on feature location optimization and integration

According to the needs of automatic and efficient detection for the urban areas, an urban area detection algorithm is proposed in this paper. First, the intelligent haze removal processing is used to reduce the interference in detection. Second, the primary feature locations of urban are extracted by the feature points. Then with the combination of the global and local constraints, the highly reliable urban locations are selected. Finally, the urban characteristic locations are integrated by the method of Gaussian rendering weighted and the final urban areas are obtained through adaptive segmentation. The algorithm is tested using Google satellite images to get accurate results. It can meet the needs for automatic and real-time detection in the remote sensing image of urban areas and greatly reduce the workload of manual interpretation.