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Granular computing based segmentation and textural analysis (GrCSTA) framework for object-based LULC classification of fused remote sensing images

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Abstract

Machine learning(ML) based techniques for Land Use Land Cover(LULC) classification is crucial for extracting valuable insights from satellite imagery. The impact of severe class imbalance problems on LULC datasets, added to the limited temporal coverage, incomplete spectral information, spatial resolution constraints, and limitations due to sensor characteristics of a single satellite, hinder the efficient capturing of complex image features. The object-based classification using Gray-level co-occurrence matrix(GLCM) and Simple Non-Iterative Clustering(SNIC) captures the textural and spectral information, respectively, to enhance the accuracy in heterogeneous landscapes and overcome the limitations of pixel-based classification, such as the sensitivity towards noise and spectral confusion in mixed pixels at the cost of increased additional computational steps. In this context, the proposed study leverages high-quality fused images with diverse temporal, spectral, and spatial information obtained by fusing Landsat-8 and Sentinel-2 satellite imageries using Spatial-and-Temporal-Adaptive-Reflectance-Fusion-Model(STARFM). Further, to solve the class imbalance problem, a Granular Computing(GrC) based Segmentation and Textural Analysis(GrCSTA) framework is proposed for reducing the number of image primitives and computations required for subsequent image analysis processes in the object-based classification. The GrCSTA framework focuses on extracting the Spatial Granules(Gs) from the fused imagery using Spatial neighborhood Granulation(SNGr), textural indices of the Gs using GLCM, and reduced textural indices using Principal Component Analysis(PCA). Gs and its reduced textural indices are input features to train the Random Forest(RF) classifier. Experimental results demonstrate that the proposed GrCSTA framework achieves comparably higher accuracy than the state-of-the-art models.

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All data used in this study are available in Google Earth engine data catalog.

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    Greetta Pinheiro & Sonajharia Minz

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Conceptualization, GP and SM; Methodology, Experiments, Validation, and Formal Analysis, GP; Resources and Supervision, SM; Writing- Original Draft, GP; Proofreading and Editing, GP,SM;

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Pinheiro, G., Minz, S. Granular computing based segmentation and textural analysis (GrCSTA) framework for object-based LULC classification of fused remote sensing images. Appl Intell 54, 5748–5767 (2024). https://doi.org/10.1007/s10489-024-05469-z

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