## 1 Introduction
Therefore, numerous researchers have employed transformer models for cloud extraction tasks in optical satellite imagery [23; 24; 25; 26]. [PERSON] et al. [27] proposed a CNN cloud detection algorithm for GF-1 satellite images. Through cascading the channel and spatial attention, it introduced a probabilistic upsampling module to merge the downsampling channels through the entire network structure. Then, dark channel transformation based on dark channel prior technology and NSCT was added to the above network [28]. Even though the attention mechanism in transformer models has demonstrated excellent performance in various domains, the direct transplantation of transformer to cloud extraction tasks does not yield satisfactory results. Some CNN-Trans-based methods connecting traditional CNN with a Transformer can increase the complexity of the model, making it challenging to find the optimal solution through optimization methods. Therefore, we design a network that integrates the strengths of deep CNN and attention, making it more suitable for imagery obtained from satellite sensors. In this article, we refined a Multiscale Soft Attention Convolutional Neural Network (MSACN) which is a multiscale deep convolutional network structure with a soft attention mechanism incorporating spatial information. Taking inspiration from the U-Net [15], ResNet [29] and attention mechanism [20], MSACN consists of two parts: a deep feature encoder module and a multi-head soft attention decoder module for cloud prediction. Compared with other networks, MSACN exploits the shallow-level information and high-level features of cloudy/non-cloudy pixels, which improves the extraction decision without any manual specific spectral information processing, since the pre-trained network can be visual objects in images to extract rich and unique high-level representations. Summarily, the contributions can be succinctly outlined as follows:  
1. We expand the scale of the ZY-3 satellite remote sensing cloud extraction dataset. To test in more complex scenarios, we augmented the dataset by incorporating a subset of clouds with snow images. The raw data are pre-extracted using the model, followedby manual refinement using Photoshop. Each image underwent meticulous selection to ensure the training data accuracy. 2. We refine a multiscale deep convolutional neural network with soft attention and spatial information for cloud segmentation from satellite remote sensing images. Following the encoder-decoder architecture, our primary enhancement lies in the incorporation of a concurrent dilated residual convolution module and a multi-head soft attention fusion between the encoding and decoding processes, respectively. 3. To evidence the validation, we employ comparative analyses with similar methods, including traditional CNN-based approaches, as well as dissimilar methods such as transformer-based models, all within the same datasets and training environment. In terms of the overall accuracy, precision, recall, F1-score and IoU, the performance of MSACN outperformed with other networks, showcasing its superior effectiveness. Meanwhile, we transplant the model to other datasets to assess its adaptability.