24 August 2017 An edge detection method with boundary reserved based on non-subsampled contourlet transform for remote sensing imagery
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During space reconnaissance applications, edge detection from remote sensing imagery plays an important role in the target recognition processing. However, traditional edge detection methods usually only utilize the high-frequency information in one image. Since low-frequency elements may be aliasing with high-frequency parts, the edges extracted may be unconnected under complex topography, different objects and imaging conditions. This paper proposes a novel image edge detection method based on Non-Subsampled Contourlet Transform (NSCT) to keep the object boundary continuously. It transforms the image into Contourlet domain in both high-frequency and low-frequency sub-bands respectively. Depending on the feature of flexible directivity reservation of an image during NSCT, the further edge extraction consists of 3 steps: firstly, the elements of the high-frequency coefficient matrix in Contourlet domain are filtered with high values left using adaptive thresholds. Then the low-frequency edge information is extracted via Canny operator from the low-frequency sub-band information. Finally, to achieve a more consistent edge image, the low-frequency edge image is achieved according to the low-frequency matrix and adopted to compensate the high-frequency image with the isolated noise points eliminated as well. The numerical simulation and practical test results show the higher effectiveness and robustness of the proposed algorithm when comparing with the classical edge detectors, such as Sobel operator, Canny operator, Log operator and Prewitt operator, etc.
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Zizheng Hua, Zizheng Hua, Chen Gong, Chen Gong, Kun Gao, Kun Gao, Su Chen, Su Chen, Yan Lu, Yan Lu, Yanqin Jia, Yanqin Jia, } "An edge detection method with boundary reserved based on non-subsampled contourlet transform for remote sensing imagery", Proc. SPIE 10395, Optics and Photonics for Information Processing XI, 1039516 (24 August 2017); doi: 10.1117/12.2272662; https://doi.org/10.1117/12.2272662

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