An intelligent color enhancement method for high-resolution remote sensing images of the coastal zone of an island

The original high-spatial-resolution remote sensing images of coastal zones of islands often exhibit a gray tone, color cast, and indistinguishable surface feature information. In response to the increasing demand for geographic information security of coastal zones of islands, this study aims to obtain timely clear remote sensing images with rich information, moderate contrast, and uniform brightness for island reefs. Hence, it proposed an intelligent color enhancement method by combining deep learning with improved histogram matching for high-spatial-resolution remote sensing images of coastal zones of islands. First, data resampling and adaptive chunking were performed to obtain thinned images. Then, the MBLLEN network was applied to enhance the thinned images with true color. Finally, an improved histogram matching method was employed for color mapping of original images, obtaining remote sensing images with consistent colors and rich details conforming to human vision. The color-matching effects of these obtained remote sensing images were evaluated using both subjective and objective methods. The results show that compared to other commonly used color-matching methods like Retinex, HE, and MASK, the method proposed in this study yielded more satisfactory results characterized by consistent colors and rich details conforming to human vision. Therefore, the proposed method can effectively improve the visual effects of high-spatial-resolution remote sensing images of coastal zones of islands, effectively retain the details of original surface features, and significantly enhance color-matching efficiency.