An improved Mahalanobis distance-based colour segmentation method for rural building recognition
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Aiming at the rapid identification of rural buildings in complex environments from high-spatialresolution images, an improved Mahalanobis distance colour segmentation method (IMDCSM) is proposed and realised in Red, Green and Blue (RGB) space. Vector sets of a lower discrete degree are obtained by filtering the colour vector sets of the building samples, and a standard ellipsoid equation can be constructed based on these vector sets. The threshold of interested colour range can be flexibly and intuitively selected by changing the shape and size of this ellipsoid. Then, according to the relationship between the location of the image pixel colour vector and the ellipsoid, all building information can be extracted quickly. To verify the effectiveness of the proposed method, unmanned aerial vehicle (UAV) images of two areas in the suburbs of Chengdu city and Deyang city were utilised as experimental data for image segmentation, and the existing colour segmentation method based on the Mahalanobis distance was selected as an indicator to assess the effectiveness of this method. The experimental results demonstrate that the completeness and correctness of this method reached 95% and 83.0%, respectively, values that are higher than those of the Mahalanobis distance colour segmentation method (MDCSM). In general, this method is suitable for the rapid extraction of rural building information, and provides a new threshold selection method for classification.
KeywordsMahalanobis distance Red Green and Blue vector Colour image segmentation Rural buildings recognition
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This work was supported by National Science and Technology Support Project of the 12th Five-Year Plan of China (Grant No.2014BAL01B04), and Sichuan Provincial Department of Land and Resources Research Project (Grant No.KJ-2018-13). The authors are grateful to the editor and reviewers for their constructive comments that have helped improve this work significantly.
- Cao HR, Zhang BL (2015) An improved definition of Mahalanobis distance with singular covariance matrix. Mathenatics in Practice and Theory (01): 226–230. (In Chinese)Google Scholar
- Gonzalez RC, Woods RE, Eddins SL (2004) Digital Image Processing Using MATLAB. Third New Jersey: Prentice Hall: pp 237–241.Google Scholar
- Guo QR, Xu JL, Sun SS, et al. (2010) Color image segmentation method based on color barycenters and K–means algorithm. Journal of Zhejiang Sci–Tech University 27(04): 580–584. (In Chinese) https://doi.org/10.3969/j.issn.1673-3851.2010.04.015 Google Scholar
- Mayer H, Wiedemann C, et al. (1997) Evaluation of automatic road extraction. International Archives of Photogrammetry and Remote Sensing 32 (3 SECT 4W2): 151–160.Google Scholar
- Ibraheem NA, Hasan MM, Khan RZ, et al. (2012) Understanding color models: a review. ARPN Journal of Science and Technology 2(3): 265–275.Google Scholar
- Liu HF, Chang QR, Li FL (2013) Urban building extraction from high–resolution multi–spectral image with object–oriented classification. Journal of Northwest A&F University(Nat. Sci. Ed.) 41(10): 221–227, 234. (In Chinese)Google Scholar
- Li XH, Wu JF, Zhang GF, et al. (2013) New color image segmentation based on watershed and region merging. Journal of Electronic Measurement and Instrument 27(03): 247–252. (In Chinese) https://doi.org/10.13207/j.cnki.jnwafu.2013.10.007 Google Scholar
- Mahalanobis PC (1936) On the generalized distance in statistics. Proceedings of the National Institute of Sciences (Calcutta) 2: 49–55.Google Scholar
- Muller S, Zaum DW (2005) Robust building detection in aerial images. International Archives of Photogrammetry and Remote Sensing 36(B2/W24): 143–148.Google Scholar
- Pang XM, Min ZJ, Kan JM (2011) Color image segmentation based on HSI and LAB color space. Journal of Guangxi University: Natural Science Edition 36(06): 976–980. (In Chinese) https://doi.org/10.3969/j.issn.1001-7445.2011.06.018 Google Scholar
- Rottensteiner F, Trinder J, Clode S, et al. (2004) Fusing airborne laser scanner data and aerial imagery for the automatic extraction of buildings in densely built–up areas. International Archives of Photogrammetry and Remote Sensing 35(B3): 512–517.Google Scholar
- Shi J, Chen CK (2011) Mahalanobis distance–based semisupervised discriminant analysis for face recognition. Journal of Beijing University of Aeronautics and Astronautics 37(12): 1589–1593. (In Chinese) https://doi.org/10.13700/j.bh.1001-5965.2011.12.013 Google Scholar
- Wang C, Chen M, Liu Y, et al. (2010) Extraction of color Doppler flow image of left heart by color image segmentation. Chinese Journal of Medical Imaging 18(3): 272–275. (In Chinese) https://doi.org/10.3969/j.issn.1005-5185.2010.03.017 Google Scholar
- Xu SH, Liu JP, Hu MY (2010) Automatic building detection in color aerial images based on region segmentation. Journal of Liaoning Technical University (Natural Science) 29(06): 1058–1061. (In Chinese)Google Scholar