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Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data

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Abstract

Texture analysis of remote sensing images has been received a substantial amount of attention as it plays a vital role in improving the classification accuracy of heterogeneous landscape. However, it is inadequately studied that how the images from different sensors with varying spatial resolutions influence the choice of textural features. This study endeavors to examine the textural features from the Landsat 8-OLI, RISAT-1, Resourcesat 2-LISS III, Sentinel-1A and Resourcesat 2-LISS IV satellite images with spatial resolution of 30, 25, 23.5, 5×20 and 5.8 m respectively, for improving land use/land cover (LULC) classification accuracy. The textural features were extracted from the aforesaid sensor data with the assistance of gray-level co-occurrence matrix (GLCM) with different moving window sizes. The best combination of textural features was recognized using standard deviations and correlation coefficients following separability analysis of LULC categories based on training samples. A supervised support vector machine (SVM) classifier was employed to perform LULC classification and the results were evaluated using ground truth information. This work demonstrates the significance of textural features in improving the classification accuracy of heterogeneous landscape and it becomes more significant as the spatial resolution improved. It is also revealed that textures are vital especially in the case of SAR data.

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Acknowledgements

The authors wish to gratefully acknowledge the NRSC, USGS and ESA for the free access to LISS III, Landsat 8-OLI and Sentinel-1A SAR data respectively.

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Authors and Affiliations

  1. Department of Physics, Indian Institute of Technology (BHU), Varanasi, 221005, India

    Varun Narayan Mishra, Rajendra Prasad & Ajeet Kumar Vishwakarma

  2. Department of Geography, Institute of Science, Banaras Hindu University, Varanasi, 221005, India

    Praveen Kumar Rai

  3. Department of Geography, Faculty of Natural Sciences, Jamia Millia Islamia, New Delhi, 110025, India

    Aman Arora

Authors
  1. Varun Narayan Mishra
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  2. Rajendra Prasad
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  3. Praveen Kumar Rai
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  4. Ajeet Kumar Vishwakarma
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  5. Aman Arora
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Correspondence to Rajendra Prasad.

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Communicated by: H. A. Babaie

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Mishra, V.N., Prasad, R., Rai, P.K. et al. Performance evaluation of textural features in improving land use/land cover classification accuracy of heterogeneous landscape using multi-sensor remote sensing data. Earth Sci Inform 12, 71–86 (2019). https://doi.org/10.1007/s12145-018-0369-z

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  • DOI: https://doi.org/10.1007/s12145-018-0369-z

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