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《Journal of Graphics》 2017-04
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Target Edge Extraction of Remote Sensing Images Based on Non-Subsampled Shearlet Transform and Improved Mathematical Morphology

WU Shihua;WU Yiquan;ZHOU Jianjiang;College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics;State Key Laboratory of CAD&CG, Zhejiang University;Beijing Key Laboratory of Urban Spatial Information Engineering;Key Laboratory of Port, Waterway and Sedimentation Engineering of the Ministry of Transport, Nanjing Hydraulic Research Institute;Key Laboratory of the Yellow River Sediment of Ministry of Water Resource, Yellow River Institute of Hydraulic Research,Yellow River Water Resources Commission;State Key Laboratory of Urban Water Resource Environment, Harbin Institute of Technology;  
In order to extract edges of target area more completely and accurately from remote sensing images, a method of target edge extraction is proposed based on improved mathematical morphology and modulus maxima of non-subsampled Shearlet transform. Firstly, the image is decomposed into high-frequency components with more edges and details and low-frequency component with fewer edges and minutiae through non-subsampled Shearlet transform. Then considering the property of coefficients of edge points under different decomposing conditions, the modulus maximum detection is performed for each sub-band of high-frequency components and the double-layer mask is adopted afterwards so as to get the high-frequency edge extraction result. Moreover, the low-frequency component is processed through the improved mathematical morphology method to get the low-frequency edge extraction result. Finally, the above two parts are fused and the final target edge image is obtained after removing the isolated points according to the regional connectivity. A large number of experimental results show that, compared with Canny method and four similar edge extraction methods, the detected edges by the proposed method are accurate, clear, complete and with abundant details. The method has strong anti-noise performance, which lays a better foundation for the following target feature extraction and recognition of remote sensing images.
【Fund】: 国家自然科学基金项目(61573183);; CAD&CG国家重点实验室开放基金项目(A1519);; 城市空间信息工程北京市重点实验室开放基金项目(2014203);; 港口航道泥沙工程交通行业重点实验室开放基金项目;; 水利部黄河泥沙重点实验室开放基金项目(2014006);; 城市水资源与水环境国家重点实验室开放基金项目(LYPK201304);; 江苏高校优势学科建设工程资助项目
【CateGory Index】: TP751
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