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Improving Object-Based Land Use/Cover Classification from Medium Resolution Imagery by Markov Chain Geostatistical Post-Classification

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  • Wenjie Wang

    (Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Weidong Li

    (Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Chuanrong Zhang

    (Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT 06269, USA)

  • Weixing Zhang

    (Department of Geography & Center for Environmental Sciences and Engineering, University of Connecticut, Storrs, CT 06269, USA)

Abstract

Land use/land cover maps derived from remotely sensed imagery are often insufficient in quality for some quantitative application purposes due to a variety of reasons such as spectral confusion. Although object-based classification has some advantages over pixel-based classification in identifying relatively homogeneous land use/cover areas from medium resolution remotely sensed images, the classification accuracy is usually still relatively low. In this study, we aimed to test whether the recently proposed Markov chain random field (MCRF) post-classification method, that is, the spectral similarity-enhanced MCRF co-simulation (SS-coMCRF) model, can effectively improve object-based land use/cover classifications on different landscapes. Four study areas (Cixi, Yinchuan and Maanshan in China and Hartford in USA) with different landscapes and classification schemes were chosen for case studies. Expert-interpreted sample data (0.087% to 0.258% of total pixels) were obtained for each study area from the original Landsat images used in object-based pre-classification and other sources (e.g., Google satellite imagery). Post-classification results showed that the overall classification accuracies of the four cases were obviously improved over the corresponding pre-classification results by 14.1% for Cixi, 5% for Yinchuan, 11.8% for Maanshan and 5.6% for Hartford, respectively. At the meantime, SS-coMCRF also reduced the noise and minor patches contained in pre-classifications. This means that the Markov chain geostatistical post-classification method is capable of improving the accuracy and quality of object-based land use/cover classification from medium resolution remotely sensed imagery in various landscape situations.

Suggested Citation

  • Wenjie Wang & Weidong Li & Chuanrong Zhang & Weixing Zhang, 2018. "Improving Object-Based Land Use/Cover Classification from Medium Resolution Imagery by Markov Chain Geostatistical Post-Classification," Land, MDPI, vol. 7(1), pages 1-16, March.
    • Handle: RePEc:gam:jlands:v:7:y:2018:i:1:p:31-:d:135069
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    References listed on IDEAS

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    1. Martin Herold & Joseph Scepan & Keith C Clarke, 2002. "The Use of Remote Sensing and Landscape Metrics to Describe Structures and Changes in Urban Land Uses," Environment and Planning A, , vol. 34(8), pages 1443-1458, August.
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    1. Sunil Kumar & Swagata Ghosh & Sultan Singh, 2022. "Polycentric urban growth and identification of urban hot spots in Faridabad, the million-plus metropolitan city of Haryana, India: a zonal assessment using spatial metrics and GIS," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8246-8286, June.
    2. John E. K. Akubia & Antje Bruns, 2019. "Unravelling the Frontiers of Urban Growth: Spatio-Temporal Dynamics of Land-Use Change and Urban Expansion in Greater Accra Metropolitan Area, Ghana," Land, MDPI, vol. 8(9), pages 1-23, August.
    3. Etido Essien & Cyrus Samimi, 2021. "Evaluation of Economic Linkage between Urban Built-Up Areas in a Mid-Sized City of Uyo (Nigeria)," Land, MDPI, vol. 10(10), pages 1-15, October.
    4. Mahdis Sadat & Mahmood Zoghi & Bahram Malekmohammadi, 2020. "Spatiotemporal modeling of urban land cover changes and carbon storage ecosystem services: case study in Qaem Shahr County, Iran," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 22(8), pages 8135-8158, December.

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