Unifying Multiscale Representations in Remote Sensing Image Color Correction With a Mamba-Conv Fusion Network

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Abstract
Optical remote sensing images (RSIs) often suffer from color distortions caused by atmospheric scattering, sensor characteristics, and illumination variations, which degrade visual quality and compromise downstream performance. Non-deep learning methods rely on empirical models and artificial parameters, and are difficult to adapt to complex and changeable remote sensing imaging conditions. Existing CNN-based methods are limited by their local receptive fields, while Transformer-based models incur high computational costs when modeling large-scale RSIs. To address these issues, a Mamba-Conv hybrid multiscale fusion network for RSI color correction is proposed in this work. The network leverages an alternating coupling mechanism between convolution and state-space modeling to unify local detail preservation and global illumination consistency, while a multiscale adaptive fusion strategy ensures coherent cross-scale feature alignment. Extensive experiments show that proposed method consistently surpasses current advanced techniques in both quantitative metrics and visual assessments, offering an effective method to alleviate color distortion in remote sensing image and enhance its interpretability and application value.
Funding Information
  • National Key Research and Development Program of China (2022YFC3302904)

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