Abstract
Environmental monitoring using image-based signal processing has emerged as a vital tool in analyzing changes in landscapes, vegetation, water bodies, and urban environments. This paper investigates the use of advanced signal processing techniques to enhance, analyze, and interpret satellite, aerial, and terrestrial images for environmental assessment. Key methodologies include multiresolution analysis, image classification, change detection algorithms, and machine learning-based segmentation. The integration of these techniques supports efficient, scalable, and automated environmental monitoring essential for sustainable development and climate response strategies.
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