Cloud obscuration in remote sensing images affects Earth observation tasks by causing blurred and incomplete surface observation information. Regarding this, cloud detection is crucial in the processing of remote sensing images. However, existing cloud detection methods present some challenges, such as missed detection of thin cloud areas and false detection caused by confusing clouds with highlighted areas such as snow and ice. To address these problems, in this paper, we proposed a cloud detection network that incorporates spectral feature enhancement and spatial-spectral feature fusion. Based on the difference in reflectivity of clouds and ground objects in the atmosphere, we proposed a short-wave infrared cloud index (SWIR-Index) and designed a feature-guided module to incorporate the spectral feature into the network and guide the training of the network to enhance the network’s ability to learn differential features of snow, ice, and clouds. To fully utilize the spectral band information and spatial features of remote sensing images, we developed a spatial-spectral feature fusion module that extracts spatial features at different scales and performs inter-spectral information fusion of spectral bands. Furthermore, we proposed a encoder-decoder feature fusion module that automatically calculates pixel weights by using a weight extraction block. The ablation study proves that our method can improve the feature extraction ability, reduce the leakage and misdetection, and improve the detection accuracy. Experimental results on Sentinel-2A images demonstrate the superior performance of the method, reaching 98.65(%) OA on WHUS2-CD dataset, 97.50(%) on S2-CMC dataset, and 92.36(%) on CloudSEN12 dataset, which outperforms other algorithms.

Rapid advances in remote sensing technology have allowed its extensive use in defense, land use planning, urban traffic monitoring, and natural disaster warning. Remote sensing technology has penetrated every aspect of modern life. However, some problems need to be solved in the use of remote sensing data, such as the presence of clouds in images. Efficient airground data transmission can be realized by performing cloud rejection on remote sensing images before satellite data transmission. Therefore, in this study, remote sensing images were analyzed, and an effective cloud detection algorithm was designed. A dense-connected-strategy-based spectral-spatial feature extraction module that can realize the independent extraction of spectral and spatial information was designed. To enhance the effective information and suppress the useless information, spatial and channel attention modules based on the self-attention mechanism were designed and added after the spectral information extraction and spatial information extraction modules, respectively. Finally, the contextual dynamic convolution module was designed to adjust the convolution kernel parameters adaptively and enhance the characterization ability of the network.

The presence of shadows in high-resolution (HR) remote-sensing images reduces object detection accuracy. To address this problem, in this paper, we proposed a deep neural network algorithm for shadow detection by using the AISD and SSAD remote-sensing shadow image datasets. To improve the ability to extract spatial information from feature maps, we developed a cross-spatial attention module that focuses on semantic information in the horizontal and vertical directions at each position point on the remote-sensing image. This module overcomes the limitations of existing technologies in accurately judging small areas and suspected shadow areas and in missing or incorrectly detected shadow areas. In addition, to improve the ability to extract shadow features and the accuracy of shadow detection in remote-sensing images, we developed a channel attention module that assigns more attention to channels that conform to the shadow color characteristics. The network architecture comprises an encoder – decoder structure, with ResNeXt50 used as the backbone for the encoder and a multi resolution parallel fusion (MRPF) designed for the decoder; cross-spatial and channel attention were incorporated into the decoder unit. Experimental results demonstrated the superior performance of the proposed algorithm, with an F1 score of 92.6% for the shadow category on the test set, thus, outperforming other algorithms and making the proposed method an effective solution for shadow detection in HR remote-sensing images.

In the field of remote sensing image, how to transmit image information more efficiently with limited bandwidth has always been a research hotspot. Compared with other ground objects, cloud pixels in remote sensing image are invalid information, so it is a meaningful research work to remove cloud before transmitting image and reduce the waste of useless information. In remote sensing image, due to the existence of thin clouds and the complexity of the underlying surface, most of the cloud detection algorithms struggle to achieve effective separation of clouds and ground objects. A deep learning (DL) cloud detection algorithm based on attention mechanism and probability upsampling has been proposed in this article. In order to enhance the information of the key areas, in the channel attention module, crucial information is highlighted in the channel dimension of the encoder, and the useless information is weakened. The spatial attention module is in the spatial dimension. The information fusion between each point in the image is strengthened. To reduce the information loss caused by the down-sampling module, a probabilistic upsampling block (PUB) is proposed to restore the image. Eventually, experiments are performed on Gaofen-1WFV data, and the results indicate that the algorithm proposed in this article has better detection results than other cloud detection algorithms in different scenarios.

Cloud detection is of great significance for the subsequent analysis and application of remote-sensing images, and it is a critical part of remote-sensing image preprocessing. In this article, we propose a cloud detection method using convolutional neural networks based on cascaded feature attention and channel attention (CFCA-Net). The CFCA-Net uses cascaded feature attention module (CFAM) to enhance the attention of the network toward important color feature and texture feature. The CFAM cascaded the color feature attention and texture feature attention module in the encoder. The CFAN-Net also uses channel attention to highlight the important information in the channel dimensions. The attention module is based on multi-scale features and uses dilated convolution with different dilation rates to obtain information about multiple receptive fields. Moreover, a loss function combined quadtree and binary cross-entropy (BCE) was also introduced to make the network focus on the edge of cloud area. We validated our CFCA-Net on the Gaofen-1 wide field-of-view (WFV) imagery dataset. The experimental results show that the CFCA-Net performs well under different scenarios, and its overall accuracy reaches 97.55%. Moreover, subjective cloud detection results also prove the effectiveness of our algorithm.