Remote Sensing Image Enhancement in Complex Mountainous Areas Using an Integrated Multi-Objective Particle Swarm Optimization

Remote sensing imaging of complex mountainous areas is significantly impacted by uneven illumination, low contrast, and blurred details due to terrain undulation and noise interference, which severely affects the quantitative remote sensing applications in mountainous environments. To address the challenges of homomorphic filtering and contrast limited adaptive histogram equalization (CLAHE) image enhancement methods, which rely on manual parameter tuning and struggle to balance multi-objective optimization, this study proposes an integrated-strategy multi-objective particle swarm optimization method for homomorphic filtering-CLAHE remote sensing image enhancement (ISMOPSO-HC). This method combines homomorphic filtering and CLAHE to construct a hybrid image enhancement framework in both the frequency and spatial domains and introduces an integrated-strategy multi-objective particle swarm optimization algorithm.The proposed method is applied to enhance Landsat 8 remote sensing images and compared with various existing enhancement techniques. This method effectively enhances the clarity and structural integrity of complex terrain textures. The findings contribute to improving the visualization and structural characterization of remote sensing images in mountainous areas, providing a solid foundation for subsequent applications in target identification, intelligent interpretation, and forest resource analysis.