Image Enhancement using CLAHE and Noise Removal for Shoreline Detection Framework

Coastal regions play a pivotal role in various aspects of human activity, including environmental conservation, disaster response, and navigation. Effective shoreline detection in remote sensing imagery is crucial for these applications, but the accuracy of shoreline detection is closely tied to the quality of the input imagery. Remote sensing images often suffer from issues related to contrast and noise, which can obscure critical shoreline features. This study presents a novel framework for shoreline detection, combining Contrast Limited Adaptive Histogram Equalization (CLAHE) and advanced noise removal techniques to address these issues. CLAHE is employed to enhance local contrast and improve visibility, while noise reduction techniques mitigate the impact of inherent noise in remote sensing data. Key objectives of this research include evaluating the impact of CLAHE on image contrast and shoreline visibility, assessing the effectiveness of noise removal techniques in enhancing image quality, and investigating the combined effect of CLAHE and noise removal on shoreline detection accuracy. The experimental results demonstrate the framework's potential for improving shoreline detection accuracy, with notable enhancements in contrast and visibility. By optimizing image quality, this framework contributes to the field of coastal management, environmental monitoring, and disaster response, offering a valuable tool for the accurate delineation of shorelines in remote sensing imagery.