Development of classification strategies for satellite images

Abstract

The quest to improve classification accuracy in satellite imagery has newlinecaptivated the focus of numerous machine learning researchers in recent years. newlineThis surge of interest is driven by the growing demand for precise and reliable newlineinformation derived from satellite data, which is critical for applications newlinespanning environmental monitoring, urban planning, disaster response, and newlineresource management. newlineRecent advancements in machine learning have significantly influenced newlinethe methodologies employed in satellite image classification. Researchers are newlineexploring and refining a variety of techniques to address the challenges posed by newlinethe inherent complexity and heterogeneity of satellite data. These methodologies newlineencompass both classical and cutting-edge approaches including classical newlineclassification algorithms like maximum likelihood classifier, and parallelepiped newlineclassifier, modern machine learning methods like deep learning, ensemble newlinemethods, transfer learning, etc., and advanced techniques like hybrid models, newlinepost-processing techniques such as spatial smoothing, contextual analysis to newlinehandle the issues like mixed pixels and noise in images. The pursuit of enhancing classification accuracy in satellite images reflects newlinea broader trend of leveraging advanced machine learning methodologies to tackle newlinecomplex real-world problems. newline