Objected-Based Structural Feature Extraction Method Using Spectral and Morphological Information

LIN Dong; QIN Zhiyuan; TONG Xiaochong; QIU Chunping; LI He

doi:10.13203/j.whugis20150627

Volume 43 Issue 5

May 2018

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Geomatics and Information Science of Wuhan University > 2018 > 43(5): 704-710. > DOI: 10.13203/j.whugis20150627

LIN Dong, QIN Zhiyuan, TONG Xiaochong, QIU Chunping, LI He. Objected-Based Structural Feature Extraction Method Using Spectral and Morphological Information[J]. Geomatics and Information Science of Wuhan University, 2018, 43(5): 704-710. DOI: 10.13203/j.whugis20150627

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Objected-Based Structural Feature Extraction Method Using Spectral and Morphological Information

1.
Institute of Geographical Spatial Information, Information Engineering University, Zhengzhou 450001, China
2.
Department of Survey and Mapping Engineering, Henan University of Urban Construction, Pingdingshan 467036, China

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The National Natural Science Foundation of China 41501506

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Author Bio:
LIN Dong, PhD candidate, specializes in high resolution imagery segmentation. E-mail: lindong_hb59@163.com
Received Date: October 22, 2015
Published Date: May 04, 2018

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Abstract

Abstract

In order to overcome the drawbacks of using either spectral or morphological features for traditional image segmentation methods, a multi-scale image segmentation method using both the spectral and morphological information is proposed. First of all, Differential Morphological Profiles are combined with spectral features to form spectral-morphological characteristics. Then, Hausdorff distance is implemented to calculate the weight of edges based on graph theory and minimum spanning tree algorithm Kruskal is applied to complete the initial segmentation of color images. Finally, the obtained segmentation result is refined by a region merging procedure with the regional heterogeneous criteria proposed in fractal network evolution. Furthermore, object-based Gray Level Co-occurrence Matrix and object-based Pixel Shape Index are proposed on the basis of segmentation results. Experimental results show that the proposed segmentation method is more effective and more efficient than eCognition software 8.0 and Meanshift algorithm. In addition, object-based Gray Level Co-occurrence Matrix and object-based Pixel Shape Index are apparently better than traditional pixel-based methods.

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