# CloudformerV3: Multi-Scale Adapter and Multi-Level Large Window Attention for Cloud Detection
## 1 Introduction  
Remote sensing technology is assuming an increasingly pivotal role in the realm of Earth observation. However, according to the International Satellite Cloud Climatology Project Flux Data (ISCCP-FD) [1], high-altitude clouds blanket 66% of the Earth's surface, posing a significant impediment to acquiring substantial surface data. This limitation curtails the potential of remote sensing technology. Therefore, within the realm of preprocessing remote sensing images, the detection of clouds assumes paramount importance. By effectively identifying clouds within remote sensing images, surface information can be more accurately extracted. This enhances the authenticity and availability of remote sensing images. Such enhancement holds tremendous practical significance, offering invaluable support for endeavors such as archaeological research [2], the detection of changes in landscape ecology [3], the identification of water bodies [4], weather forecasting [5], and geological landscape analysis [6].  
In cloud detection tasks, several challenges persist, such as the difficulty in distinguishing between thin clouds and the Earth's surface, the tendency for bright surfaces like ice and snow to be mistakenly identified as cloud layers, and the challenges in detecting small and fragmented clouds. Therefore, addressing the issue of unclear boundaries betweenthin clouds and the Earth's surface in complex scenarios, this paper continues to delve into the application of the transformer in the cloud detection task. Based on Cloudformer [7] and CloudformerV2 [8], this study suggests using CloudformerV3 to recognize clouds in high-resolution remote sensing image data.  
The three important areas covered by CloudformerV3's principal contributions are as follows:  
1. Multi-Scale Adapter Incorporation in the Encoder: Introducing a multi-scale adapter within the encoder enhances the synergy between the pretrained natural image-based backbone and the remote sensing image cloud detection process. This collaboration facilitates multi-level feature extraction, allowing the model to gain a more profound understanding of image structure and characteristics that are pertinent to the task. Furthermore, through the use of an adapter, downstream tasks and other prior information can be integrated into the model. This allows for the adaptation of multi-channel data information without compromising the use of models pretrained on a substantial amount of natural images;
2. Multi-Level Large Window Attention Enhanced Decoder Mechanism: In the decoder stage, a novel approach is adopted involving the interaction of low-resolution feature maps with high-resolution feature maps through large window attention, which establishes connections across different levels of feature maps, thereby enabling the model to enhance contextual understanding. This process encompasses incremental layer-by-layer upsampling and fusion with higher-level feature maps. As a result, the decoder comprehensively integrates feature information across multiple levels, thereby intensifying the ability to detect cloud edges;
3. Integration of Dark and Bright Channel Prior to Information: During the data preprocessing phase, the dark channel and bright channel prior information are computed and then integrated into the generalized backbone through an adapter. This infusion equips the model with prior feature information that significantly enhances cloud detection capabilities. In particular, this enhancement contributes to distinguishing thin clouds from the ground surface with greater precision.  
The remainder of this paper is organized as follows. Section 2 introduces the related work. Section 3 presents the design details of our proposed network. Section 4 provides the relevant experiments and setups. Section 5 summarizes our approach, and Section 6 presents the outlook.