Published Paper Details:

Low contrast is common in remote sensing pictures. Although numerous contrast enhancement techniques have been suggested in recent literature, remote sensing image contrast enhancement effectiveness and durability remain a problem. The goal of this research is to propose a new self-adaptive histogram compacting transform based contrast enhancement technique for remote sensing pictures that meets the needs of automation, robustness, and efficiency in applications. First, the merging cost, moderate global brightness, and the entropy contribution of grey levels are used to optimise the histogram of an input picture into compact and continuous status. Then, to capture more details while grey extending using the linear stretch, a local remapping method is suggested. Finally, to improve contrast in both bright and dark regions, a dual-gamma transform is suggested. The suggested approach outperforms state-of-the-art methods and retains resilience in a variety of situations, as shown by experimental and comparative findings. It offers a practical method for automated contrast enhancement in remote sensing images.