Variogram texture extraction and classification of high resolution remote sensing images based on multi-resolution segmentation
LIU Changzhen1, SHU Hong1,2, ZHANG Zhi3, MA Guorui1
1. State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China;
2. Suzhou Institute of Wuhan University, Suzhou 215123, China;
3. School of Public Administration, China University of Geosciences (Wuhan), Wuhan 430074, China
As an effective tool for extracting the texture, the variogram can be used to describe the properties of structure and randomness of the images. The utilization of the traditional variogram texture extraction method with a moving window has the border effect and also has difficulty in determining the appropriate window size. To solve this problem, the authors tentatively selected the WorldView-2 image of the bare rocks in Yingisar County, Xinjiang, extracted the variogram textures based on three scale segmentation results with multi-resolution segmentation algorithm, and then superimposed them on the original multi-spectral images for lithological discrimination. The authors further compared the results of multi-resolution segmentation with moving window. The experimental results show that the texture information extraction based on segmentation could eliminate the border effect, relieve the shadow effect and improve the accuracy of lithological classification. It is found that there are some differences in identifying the effects of extracting the textures on different segmentation scales based on multi-resolution segmentation. The method proposed in this paper is more stable and reasonable than that of moving window method once an appropriate segmentation scale is set.
刘昌振, 舒红, 张志, 马国锐. 基于多尺度分割的高分遥感图像变异函数纹理提取和分类[J]. 国土资源遥感, 2015, 27(4): 47-53.
LIU Changzhen, SHU Hong, ZHANG Zhi, MA Guorui. Variogram texture extraction and classification of high resolution remote sensing images based on multi-resolution segmentation. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(4): 47-53.
[1] Matheron G.Principles of geostatistics[J].Economic Geology, 1963, 58(8):1246-1266.[2] Curran P J.The semivariogram in remote sensing:An introduction[J].Remote Sensing of Environment, 1988, 24(3):493-507.[3] Woodcock C E, Strahler A H, Jupp D L B.The use of variograms in remote sensing:I.Scene models and simulated images[J].Remote Sensing of Environment, 1988, 25(3):323-348.[4] Sertel E, Kaya S, Curran P J.Use of semivariograms to identify earthquake damage in an urban area[J].IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(6):1590-1594.[5] Feng Y M, Li Z Y, Tokola T.Estimation of stand mean crown diameter from high-spatial-resolution imagery based on a geostatistical method[J].International Journal of Remote Sensing, 2010, 31(2):363-378.[6] Yue A, Zhang C, Yang J, et al.Texture extraction for object-oriented classification of high spatial resolution remotely sensed images using a semivariogram[J].International Journal of Remote Sensing, 2013, 34(11):3736-3759.[7] Atkinson P M, Lewis P.Geostatistical classification for remote sensing:An introduction[J].Computers and Geosciences, 2000, 26(4):361-371.[8] Chica-Olmo M, Abarca-Hernandez F.Computing geostatistical image texture for remotely sensed data classification[J].Computers and Geosciences, 2000, 26(4):373-383.[9] 黄颖端, 李培军, 李争晓.基于地统计学的图像纹理在岩性分类中的应用[J].国土资源遥感, 2003, 15(3):45-49.doi:10.6046/gtzyyg.2003.03.11. Huang Y D, Li P J, Li Z X.The application of geostatistical image texture to remote sensing lithological classification[J].Remote Sensing for Land and Resources, 2003, 15(3):45-49.doi:10.6046/gtzyyg.2003.03.11.[10] Berberoglu S, Lloyd C, Atkinson P, et al.The integration of spectral and textural information using neural networks for land cover mapping in the mediterranean[J].Computers and Geosciences, 2000, 26(4):385-396.[11] Balaguer A, Ruiz L A, Hermosilla T, et al.Definition of a comprehensive set of texture semivariogram features and their evaluation for object-oriented image classification[J].Computers and Geosciences, 2010, 36(2):231-240.[12] Balaguer B A, Ruiz L A, Hermosilla T, et al.Using semivariogram indices to analyse heterogeneity in spatial patterns in remotely sensed images[J].Computers and Geosciences, 2013, 50:115-127.[13] Baatz M, Schäpe A.Multiresolution segmentation:An optimization approach for high quality multi-scale image segmentation[J].Angewandte Geographische Information Sverarbeitung, 2000, 12(12):12-23.[14] 何少林, 徐京华, 张帅毅.面向对象的多尺度无人机影像土地利用信息提取[J].国土资源遥感, 2013, 25(2):107-112.doi:10.6046/gtzyyg.2013.02.19. He S L, Xu J H, Zhang S Y.Land use classification of object-oriented multi-scale by UAV image[J].Remote Sensing for Land and Resources, 2013, 25(2):107-112.doi:10.6046/gtzyyg.2013.02.19.[15] 徐京萍, 赵建华, 张丰收, 等.面向对象的池塘养殖用海信息提取[J].国土资源遥感, 2013, 25(1):82-85.doi:10.6046/gtzyyg.2013.01.15. Xu J P, Zhao J H, Zhang F S, et al.Object-oriented information extraction of pond aquaculture[J].Remote Sensing for Land and Resources, 2013, 25(2):107-112.doi:10.6046/gtzyyg.2013.01.15.[16] Hay G J, Blaschke T, Marceau D J, et al.A comparison of three image-object methods for the multiscale analysis of landscape structure[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2003, 57(5):327-345.[17] Benz U C, Hofmann P, Willhauck G, et al.Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information[J].ISPRS Journal of Photogrammetry and Remote Sensing, 2004, 58(3):239-258.[18] Definiens A G.Definiens Ecognition Developer 8 User Guide[M].Definens AG, Munchen, Germany, 2009.
2015, Vol. 27 
