High-resolution remote sensing image segmentation based on improved superpixel and marker watershed
ZHANG Rui1(), YOU Shucheng1(), DU Lei1, LU Jing1, HE Yun1, HU Yong2
1. Land Satellite Remote Sensing Application Center, Ministry of Natural Resources, Beijing 100048, China 2. Chongqing Institute of Surveying and Monitoring for Planning and Natural Resources, Chongqing 400120, China
Image segmentation is a key step in object-oriented analysis of high resolution images and plays an important role in information extraction accuracy. In order to improve the segmentation performance of object-oriented algorithms for high-resolution remote sensing images, this paper proposes a segmentation method (PCSLIC-MW) to improve the superpixel and marker watershed, including feature fusion, superpixel initial segmentation, and control marker watershed segmentation. In the phase of superpixel segmentation, a new distance measure calculation rule is proposed, which combines color space, spatial position information and phase consistency texture feature. And then the gray value of each patch is calculated after superpixel segmentation, image reconstruction after segmentation, and morphological extension technology is used to extract local minimum (H-minima) so as to control the number of segmentation regions. The over-cutting produced by the traditional mathematical morphologic watershed segmentation algorithm is optimized and improved. The reconstructed image is conducted by Gaussian filter, and then the control marker watershed algorithm is used to re-segment the reconstructed image. For experiment, ZY3-02 satellite image and airborne aerial image are adopted to verify the proposed method, the precision and recall rate are used to evaluate the segmentation accuracy, and the results are compared with those of other segmentation methods to prove the segmentation effectiveness of the proposed method.
张锐, 尤淑撑, 杜磊, 禄競, 何芸, 胡勇. 基于改进超像素和标记分水岭的高分辨率遥感影像分割方法[J]. 国土资源遥感, 2021, 33(1): 86-95.
ZHANG Rui, YOU Shucheng, DU Lei, LU Jing, HE Yun, HU Yong. High-resolution remote sensing image segmentation based on improved superpixel and marker watershed. Remote Sensing for Land & Resources, 2021, 33(1): 86-95.
Liu Y, Fu Z Y, Zheng F B. Review on high resolution remote sensing image classification and recognition[J]. Journal of Geo-Information Science, 2015,17(9):1080-1091.
Yao B X, Huang L, Xu Y S. A high resolution remote sensing image segmentation method based on superpixel and graph theory[J]. Remote Sensing for Land and Resources, 2019,31(3):72-79.doi: 10.6046/gtzyyg.2019.03.10.
Ming D P, Qiu Y F, Zhou W. Applying spatial statistics into remote sensing pattern recognition:With case study of cropland extraction based on GeOBIA[J]. Acta Geodaetica et Cartographica Sinica, 2016,45(7):825-833.
Chen C L, Wu G. Evaluation of optimal segmentation scale with object-oriented method in remote sensing[J]. Remote Sensing Technology and Application, 2011,26(1):96-102.
Guo L, Pei Z Y, Wu Q, et al. Application of method and process of object-oriented land use-cover classification using remote sensing images[J]. Transactions of the CSAE, 2010,26(7):194-198.
Yan P F, Ming D P. Segmentation of high spatial resolution remotely sensed data using watershed with self-adaptive parameterization[J]. Remote Sensing Technology and Application, 2018,32(2):321-330.
Xie X, Wang H, Zhang X F. A survey of partial sums algorithms in image thresholding techniques[J]. Journal of Xi’an University of Posts and Telecommunications, 2011,16(3):1-5,13.
Zhu J J, Du X P, Fan X T, et al. Multi-scale edge detection and multi-scale segmentation of imagery[J]. Geography and Geo-Information Science, 2013,29(2):45-48,127.
Liu Y X, Li M C, Mao L. An algorithm of multi-spectral remote sensing image segmentation based on edge information[J]. Journal of Remote Sensing, 2006,10(3):350-356.
Chen M, Zhu Q, Zhu J, et al. Interest point detection for multispectral remote sensing image using phase congruency in illumination space[J]. Acta Geodaetica et Cartographica Sinica, 2016,45(2):178-185.
Wei C T, Li C L, Yang X W, et al. Road feature detection based on phase congruency using remote sensing image[J]. Geography and Geo-Information Science, 2011,27(4):62-66.
Zhao P F, Zhou S G, Yi Y, et al. Classification method of hyperspectral remote sensing image based on SLIC and active learning[J]. Computer Engineering and Applications, 2017,53(3):183-187.
[14]
Tarabalka Y, Chanussot J, Benediktsson J A. Segmentation and classification of hyperspectral images using watershed transformation[J]. Pattern Recognition, 2010,43(7):2367-2379.
[15]
Wang M, Yuan S G, Pan J, et al. Seamline determination for high resolution orthoimage mosaicking using watershed segmentation[J]. Photogrammetric Engineering and Remote Sensing, 2016,82(2):121-133.
[16]
Umesh Adiga P S, Chaudhuri B B. An efficient method based on watershed and rule-based merging for segmentation of 3-D histo-pathological images[J]. Pattern Recognition, 2001,34(7):1449-1458.
[17]
Soille P. Morphological image analysis principle and application[M]. Berlin,Germany:Springer Verlag, 1999: 123-140.
[18]
Achanta R, Shaji A, Simth K, et al. SLIC superpixels compared to state of the art superpixel methods[J]. IEEE Transactions on Software Engineering, 2012,34(11):2274-2282.
Yuan Y H, Li Y, Zhao X M. High-resolution panchromatic remote sensing image segmentation based on spectral clustering[J]. Chinese Journal of Scientific Instrument, 2016,37(7):1656-1664.
Xiao P F, Feng X Z, Zhao S H, et al. Segmentation of high-resolution remotely sensed imagery based on phase congruency[J]. Acta Geodaetica et Cartographica Sinica, 2007,36(2):146-151.
Jia C Y, Li W H, Li X C. High resolution remote sensing image segmentation based on weight adaptive fractal net evolution approach[J]. Remote Sensing for Land and Resources, 2013,25(4):22-25.doi: 10.6046/gtzyyg.2013.04.04.
Yu B, Niu Z, Wang L, et al. An unsupervised method of extracting constructions from color remote sensed image based on mean shift and neutrosophic set[J]. Spectroscopy and Spectral Analysis, 2013,33(4):1071-1075.
pmid: 23841431