Land Cover Land Use Image Classification using Deep Learning Techniques

Abstract

Land Use and Land Cover (LULC) classification of satellite images are a crucial process newlinefor understanding and managing natural resources, urban planning, and environmental newlinemonitoring. The classification involves analyzing satellite imagery to categorize different newlineland cover types, such as forests, water bodies, urban areas, and agricultural lands. This newlineprocess uses various remote sensing techniques and algorithms, including supervised and newlineunsupervised classification methods, to extract meaningful information from the images. newlineInitially, this research work focuses on the challenges involved in pixel level classification. newlineThese approaches typically classify each pixel independently based solely on its spectral newlineproperties, without considering the spatial context or relationships with neighboring pixels. newlineThis lack of contextual understanding can result in misclassification, particularly in newlineheterogeneous landscapes where similar spectral signatures may belong to different land newlinecover types, or where the same land cover type may exhibit a range of spectral responses. newlineTo address these challenges, a Fusion-based Multi-class Classification (FMC) model is newlineemployed, incorporating advanced pre-processing techniques. Initially, closing and newlineopening morphological operations are applied to exploit contextual pixel information. This newlinestep helps to refine the image by emphasizing relevant structures and reducing noise. newlineSubsequently, a fusion technique combines these enhanced images to produce a detailed, newlinedenoised image. This comprehensive pre-processing stage ensures that the data is wellprepared newlinefor accurate analysis. The final step involves a supervised classification approach, newlinewhich categorizes the images into seven distinct land cover classes, leveraging the newlineenhanced clarity and contextual information from the pre-processed images to improve newlineclassification accuracy. newlineSecondly, this research finds that pixel-based classification often struggles to achieve high newlineaccuracy in high-resolution scene images due to their complexity and var