Lane detection is an important aspect of autonomous driving environment perception. Traditionally, lane detection has been regarded as a semantic segmentation task, and the geometric characteristics and position information of lanes have been ignored. Different from previous models, we proposed a model to capture the high-level semantic features and low-level position features of lanes by adopting two modules in the row and column. In the horizontal direction, we utilized line shape self-attention to capture the long-distance dependencies of lanes, which is crucial due to the slender shape of lanes, while reducing unnecessary computational resources to obtain irrelevant features. We used position information vectors encoding in the Key, Query, and Value modules in the transformer to enable considering the position information to explore potential location associations between lane and employed it for the vertical direction. In the Tusimple benchmark test, this method achieved an accuracy rate of 96.74%, demonstrating good competitiveness compared with existing methods.

The rich spectral information in hyperspectral images (HSIs) requires effective joint extraction of spectral and spatial features for super-resolution (SR) tasks. To better capture spectral–spatial representations and restore edge textures, we propose the differentiation and cross-domain feature extraction network (DCDFENet) for HSI SR. Its core module enhances reconstruction by leveraging cross-domain interactions and differential features. DCDFE consists of three components: cross-domain and multiscale feature extraction (CDMSFE), which uses separated 3D convolutions and cross-domain connections to extract complementary features at multiple scales; spectral feature differentiation highlighting attention (SFDHA), which emphasizes significant spectral variations via differentiation and attention mechanisms; and edge feature extraction (EFE), which integrates Laplacian filtering and an extreme value feaure extraction (EVFE) to enhance spatial edge textures. Additionally, a loss function based on singular value decomposition further mitigates spectral distortion. Experiments on three benchmark datasets demonstrate that our method achieves superior performance on PSNR, MPSNR, SSIM, and SAM metrics.

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.

Cloud detection is a challenging task but plays a major role for remote sensing image processing. Due to the diversity of cloud and the complexity of underlying surfaces, most of the current cloud detection methods still face great challenges, especially in detecting the thin cloud. Therefore, we propose a method to detect cloud pixels in GaoFen-1 WFV images. In our method, the deep encoder-decoder network is used to learn the multi-scale global features. So that the high-level semantic information obtained in the process of feature learning is integrated with low-level spatial information to classify images into cloud and non-cloud regions. In addition, Up and Down blocks using Harr wavelet transform are designed to fully exploit the structural information of images, and especially the texture information of the cloud can be learned targetedly. The experimental results indicate that the network using Up and Down blocks performs well under different scenes.

Cloud detection is a critical part of remote sensing images preprocessing, which can be regarded as an image pixel-segmentation problem. In recent years, because of effective performance, convolutional neural network is widely used in image segmentation. This paper proposed a cloud detection method based on convolutional neural network, not only adding Gabor feature extraction module to further extract the detail information in the low-level features but also mining the correlation between high-level features through the channel attention module. In order to evaluate our method, experiments were carried on the Gaofen-1 WFV dataset containing different types of clouds over various underlying. The results show that our method has higher accuracy rate and lower false alarm rate comparing to several state-of-the-art image segmentation network.

With the development of deep learning, many methods on image denoising have been proposed processing images on a fixed scale or multi-scale which is usually implemented by convolution or deconvolution. However, excessive scaling may lose image detail information, and the deeper the convolutional network the easier to lose network gradient. Diamond Denoising Network (DmDN) is proposed in this paper, which mainly based on a fixed scale and meanwhile considering the multi-scale feature information by using the Diamond-Shaped (DS) module to deal with the problems above. Experimental results show that DmDN is effective in image denoising.

The coherent imaging method of synthetic aperture radar (SAR) brings SAR images with strong and randomly distributed speckle, which causes great interference to subsequent applications. To deal with the affected images, we propose a multiconnection network incorporating wavelet features (MCN-WF) to despeckle the images and then evaluate the results. On the one hand, simplified dense connections in and among Dense Blocks (DBs) utilize the features extracted from the network at different scales to produce despeckled images with more details. On the other hand, performing feature pre-extraction on images by wavelet transform can not only indirectly control the convergence direction of the network by modifying the loss function but also reduce the size of the feature maps to accelerate the speed of the network processing. The experimental results show that the new method has a better performance in terms of despecking, image texture structure preservation, and processing efficiency.

Super-resolution reconstruction technology has important scientific significance and application value in the field of image processing by performing image restoration processing on one or more low-resolution images to improve image spatial resolution. Based on the SCSR algorithm and VDSR network, in order to further improve the image reconstruction quality, an image super-resolution reconstruction algorithm combined with multi-residual network and multi-feature SCSR(MRMFSCSR) is proposed. Firstly, at the sparse reconstruction stage, according to the characteristics of image blocks, our algorithm extracts the contour features of non-flat blocks by NSCT transform, extracts the texture features of flat blocks by Gabor transform, then obtains the reconstructed high-resolution (HR) images by using sparse models. Secondly, according to improve the VDSR deep network and introduce the feature fusion idea, the multi-residual network structure (MR) is designed. The reconstructed HR image obtained by the sparse reconstruction stage is used as the input of the MR network structure to optimize the high-frequency detail residual information. Finally, we can obtain a higher quality super-resolution image compared with the SCSR algorithm and the VDSR algorithm.