Cloud cover significantly decreases the quality of Optical Remote Sensing (ORS) images, adversely impacting its effectiveness in geographic monitoring, disaster prevention, and advanced visual applications. This phenomenon has made cloud removal a critical preprocessing step in ORS image processing. This paper comprehensively reviews cloud removal techniques and classifies them based on the type of auxiliary data used: single-image, multimodal, and multitemporal. The discussed methods include physical modeling, deep learning, multispectral analysis, and Synthetic Aperture Radar (SAR) fusion strategies. This paper analyzes the core concepts and fundamental processes of these techniques and addresses the challenges encountered in actual scenarios. The paper also includes future research directions. Moreover, the paper outlines the benchmark datasets and evaluation metrics commonly used in cloud removal, thereby establishing a standardized reference for algorithm development and performance evaluation. A thorough comparative analysis was performed to assess their performance variations using visualization outcomes from the most recent and representative methodologies.

Haze, generated by floaters (semitransparent clouds, fog, snow, etc.) in the atmosphere, can significantly degrade the utilization of remote sensing images (RSIs). However, the existing techniques for single image dehazing rarely consider that the haze is superimposed by floaters and shadow, and they often aggravate the degree of the haze shadow and dark region. In this paper, a single RSI dehazing method based on robust light-dark prior (RLDP) is proposed, which utilizes the proposed hybrid model and is robust to outlier pixels. In the proposed RLDP method, the haze is first removed by a robust dark channel prior (RDCP). Then, the shadow is removed with a robust light channel prior (RLCP). Further, a cube root mean enhancement (CRME)-based stable state search criterion is proposed for solving the difficult problem of patch size setting. The experiment results on benchmark and Landsat 8 RSIs demonstrate that the RLDP method could effectively remove haze.

Remote sensing images (RSIs) are widely used in the fields of geological resources monitoring, earthquake relief, and weather forecasting, but they are easily nullified due to haze cover. Transformer-based image dehazing model can better remove the haze in RSIs and improve the clarity of RSIs. However, due to the insufficient ability to extract detailed information, the model performs poorly in the case of thick haze. To solve this problem, this letter introduces an additional self-attention mechanism to help the model acquire more detailed information based on the existing Transformer-based image dehazing model and introduces an adapter module to improve the model’s fitting capacity with newly added content. Experimental results on benchmark RSIs indicate that the proposed method yields an average improvement of 0.95 in PSNR and 0.6% in SSIM for light haze removal. Notably, the method exhibits a significant enhancement of 1.34 in PSNR and 1.9% in SSIM for the removal of thick haze, underscoring its advantage in heavy haze conditions. The source code can be accessed via https://github.com/Eric3200C/ASTA.