As buildings constitute the main component of urban areas, which can provide several kinds of information. In this paper, a building extraction method based on the high resolution remote sensing image via Gabor filter and multi-orientation π local binary pattern (LBP) operator is proposed to aim at the application requirements of rapid, accurate urban planning and visual management. At first, the multi-dimensional texture features are extracted by using Gabor filter for the original image. Further, some training samples are obtained by multi-orientation π LBP operator at different orientations. Finally, the pixel-level discrimination is conducted for texture features, and achieves the location and shape of buildings. Experimental results demonstrate that the overall extraction accuracy has reached 94%, and the extracted results coincide with the distribution of each building, the proposed method is accurate to complete the task of building extraction, and has an excellent applicability for land management.
Texture recognition is a key topic in many applications of image analysis; many techniques have been proposed to measure the characteristics of this field. Among them, texture energy extracted with the “Tuned” mask is a rotation and scale invariant texture descriptor. However, the tuning process is computationally intensive and easily to trap into local optimum. In the proposed approach, how to obtain the “Tuned” mask is viewed as a combinatorial optimization problem and the optimal mask is acquired by maximizing the texture energy value via a newly proposed cuckoo search (CS) algorithm. Experimental results on samples and images show that the proposed method is suitable for texture recognition, the recognition accuracy is higher than genetic algorithm (GA) and particle swarm optimization (PSO) optimized “Tuned” mask scheme, and the water areas can be well recognized from the original image. It is a robust and efficient method to obtain the optimal “Tuned” mask for texture analysis.
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