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http://dx.doi.org/10.7780/kjrs.2021.37.6.3.9

Object-based Image Classification by Integrating Multiple Classes in Hue Channel Images  

Ye, Chul-Soo (Department of Aviation and IT Convergence, Far East University)
Publication Information
Korean Journal of Remote Sensing / v.37, no.6_3, 2021 , pp. 2011-2025 More about this Journal
Abstract
In high-resolution satellite image classification, when the color values of pixels belonging to one class are different, such as buildings with various colors, it is difficult to determine the color information representing the class. In this paper, to solve the problem of determining the representative color information of a class, we propose a method to divide the color channel of HSV (Hue Saturation Value) and perform object-based classification. To this end, after transforming the input image of the RGB color space into the components of the HSV color space, the Hue component is divided into subchannels at regular intervals. The minimum distance-based image classification is performed for each hue subchannel, and the classification result is combined with the image segmentation result. As a result of applying the proposed method to KOMPSAT-3A imagery, the overall accuracy was 84.97% and the kappa coefficient was 77.56%, and the classification accuracy was improved by more than 10% compared to a commercial software.
Keywords
Object-based image classification; HSV color model; Image segmentation;
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1 Blaschke, T., G.J. Hay, M. Kelly, S. Lang, P. Hofmann, E. Addink, R. Queiroz Feitosa, F. van der Meer, H. van der Werff, F. van Coillie, and D. Tiede, 2014. Geographic object-based image analysistowards a new paradigm, ISPRS Journal of Photogrammetry and Remote Sensing, 87: 180-191.   DOI
2 Lee, D.G., J.H. You, and H.J. Lee, 2018. Comparison of geospatial feature extraction process on object based classification method using KOMPSAT-3A satellite image, Journal of the Korean Society for Geospatial Information Science, 26(3): 13-21 (in Korean with English abstract).   DOI
3 Magpantay, A.T., R.T. Adao, J.L. Bombasi, A.C. Lagman, E.V. Malasaga, and C.S. Ye, 2019. Analysis on the effect of spectral index images on improvement of classification accuracy of Landsat-8 OLI image, Korean Journal of Remote Sensing, 35(4): 561-571 (in Korean with English abstract).   DOI
4 Seong, S.K., S.I. Na, and J.W. Choi, 2020. Assessment of the FC-DenseNet for crop cultivation area extraction by using RapidEye satellite imagery, Korean Journal of Remote Sensing, 36(5-1): 823-833 (in Korean with English abstract).   DOI
5 Ye, C.S., 2020. Evaluating the contribution of spectral features to image classification using class separability, Korean Journal of Remote Sensing, 36(1): 55-65 (in Korean with English abstract).   DOI
6 Choi, S.K., S.K. Lee, Y.B. Kang, S.K. Seong, D.Y. Choi, and G.H. Kim, 2020. Applicability of image classification using deep learning in small area: case of agricultural lands using UAV image, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, 38(1): 23-33 (in Korean with English abstract).   DOI
7 Na, H.S. and J.S. Lee, 2014. Analysis of land cover characteristics with object-based classification method - focusing on the DMZ in Inje-gun, Gangwon-do, Journal of the Korean Association of Geographic Information Studies, 17(2): 121-135 (in Korean with English abstract).   DOI
8 Kucharczyk, M., G.J. Hay, S. Ghaffarian, and C.H. Hugenholtz, 2020. Geographic object-based image analysis: a primer and future directions, Remote Sensing, 12(12): 2012.   DOI
9 Lee, S. and J. Kim, 2019. Land cover classification using sematic image segmentation with deep learning, Korean Journal of Remote Sensing, 35(2): 279-288 (in Korean with English abstract).   DOI
10 Lee, S.H. and M.J. Lee, 2020. A study on deep learning optimization by land cover classification item using satellite imagery, Korean Journal of Remote Sensing, 36(6-2): 1591-1604 (in Korean with English abstract).   DOI
11 Rhee, S.Y., W.S. Jeon, and H. Choi, 2018. Analysis of deep learning applicability for KOMPSAT-3A satellite image classification, Journal of the Korean Society for Geospatial Information Science, 26(4): 69-76 (in Korean with English abstract).   DOI
12 Shin, J.S., T.H. Lee, P.M. Jung, and H.S. Kwon, 2015. A study on land cover map of UAV imagery using an object-based classification method, Journal of the Korean Society for Geospatial Information Science, 23(4): 25-33 (in Korean with English abstract).   DOI
13 Blaschke, T., 2010. Object based image analysis for remote sensing, ISPRS Journal of Photogrammetry and Remote Sensing, 65: 2-16.   DOI
14 Song, J.Y., J.C. Jeong, and P.S.H. Lee, 2018. Development of a classification method for forest vegetation on the stand level, using KOMPSAT-3A imagery and land coverage map, Korean Journal of Environment and Ecology, 32(6): 686-697 (in Korean with English abstract).   DOI
15 Ye, C.S., 2021. Improvement of object-based image classification using hue channel class merging, 2021 Fall Online Conference of the Korean Society for Remote Sensing, KOR, Oct. 20-22.