Satellites equipped with optical sensors capture high-resolution imagery, providing valuable insights into various environmental phenomena. In recent years, there has been a surge of research focused on addressing some challenges in remote sensing, ranging from water detection in diverse landscapes to the segmentation of mountainous and terrains. Ongoing investigations goals to enhance the precision and efficiency of satellite imagery analysis. Especially, there is a growing emphasis on developing methodologies for accurate water body detection, snow and clouds, important for environmental monitoring, resource management, and disaster response. Within this context, this paper focus on the cloud segmentation from remote sensing imagery. Accurate remote sensing data analysis can be challenging due to the presence of clouds in optical sensor-based applications. The quality of resulting products such as applications and research is directly impacted by cloud detection, which plays a key role in the remote sensing data processing pipeline. This paper examines seven cutting-edge semantic segmentation and detection algorithms applied to clouds identification, conducting a benchmark analysis to evaluate their architectural approaches and identify the most performing ones. To increase the model's adaptability, critical elements including the type of imagery and the amount of spectral bands used during training are analyzed. Additionally, this research tries to produce machine learning algorithms that can perform cloud segmentation using only a few spectral bands, including RGB and RGBN-IR combinations. The model's flexibility for a variety of applications and user scenarios is assessed by using imagery from Sentinel-2 and Landsat-8 as datasets. This benchmark can be reproduced using the material from this github link: https://github.com/toelt-llc/cloud_segmentation_comparative.

翻译：配备光学传感器的卫星能够捕获高分辨率影像，为我们理解各种环境现象提供宝贵见解。近年来，针对遥感领域挑战的研究激增，涵盖从不同地貌中的水体检测到山区及地形分割。当前研究致力于提升卫星影像分析的精度与效率，尤其注重开发精准的水体、积雪及云层检测方法，这对环境监测、资源管理和灾害响应至关重要。在此背景下，本文聚焦于遥感影像中的云分割问题。在基于光学传感器的应用中，云层的存在常使遥感数据分析面临挑战。云检测作为遥感数据处理流程的关键环节，直接影响着应用与研究的最终产品质量。本文考察了七种前沿的语义分割与检测算法在云识别中的应用，通过基准分析评估其架构方法并识别性能最优的算法。为增强模型适应性，本文分析了训练期间使用的影像类型和光谱波段数量等关键要素。此外，本研究尝试开发仅需少量光谱波段（如RGB及RGBN-IR组合）即可执行云分割的机器学习算法。通过使用Sentinel-2和Landsat-8影像作为数据集，评估模型在不同应用场景和用户情境下的灵活性。本基准分析可通过以下GitHub链接进行复现：https://github.com/toelt-llc/cloud_segmentation_comparative。