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Land Cover Mapping and Change Analysis Using Optimized Random Forest Classifier Incorporating Fusion of Texture and Gabor Features

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Abstract

Comprehensive land cover information is valuable in complex land cover mapping. Optical satellite data play an essential role for monitoring dynamic land surface processes in a timely manner. Sentinel-2A is one of the advancements in optical satellite data possessing multi-spectral imaging and high resolution which supports the monitoring of distinct land cover classes. The fair selection of appropriate input features, which may be as important as the choice of data and classifier, is another ongoing study area in addition to the progress of classification algorithms. In the past studies, the role of distinct features like MSI bands, spectral indices, GLCM (gray-level co-occurrence matrix) features and Gabor features have been analyzed individually and each of the feature set have shown their own significance, but the value proposition of all these features collectively in land cover mapping still needs to be explored. For satellite image classification, distinct machine learning algorithms have already been explored and current trend has shifted towards deep learning. But owing to deep learning models’ computationally expensive nature, requirement of expert knowledge, and abundance training data, machine learning techniques are preferred choices due to their lower computational complexity and higher interpretability capabilities compared to deep learning models. Hence, in this work, Sentinel-2A data-based land cover mapping has been executed by utilizing random forest (RF) classifier. Different feature sets have been composed and evaluated for achieving the maximum mapping accuracy. The best feature set has been selected for the classification model. The RF hyper-parameters have also been optimized using Bayesian optimization to achieve the best classification accuracy. The developed land cover mapping model has been applied on a four-year time series data (2018–2021) of Roorkee region of Uttarakhand, to explore the changes appeared in the past four years in distinct land cover classes.

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Authors and Affiliations

  1. Geomatics Engineering Group, Department of Civil Engineering, Indian Institute of Technology, Roorkee, India

    Ashish Kumar & R. D. Garg

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  1. Ashish Kumar
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Kumar, A., Garg, R.D. Land Cover Mapping and Change Analysis Using Optimized Random Forest Classifier Incorporating Fusion of Texture and Gabor Features. SN COMPUT. SCI. 4, 685 (2023). https://doi.org/10.1007/s42979-023-02111-6

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