Cloud detection is an important prerequisite for remote sensing image application. Any remote sensing image from which the information of ground object could be obtained will inevitably be preprocessed on cloud occlusion. In the traditional method, the segmentation of cloud and its shadow will be affected by the complex background. In the detection process, due to insufficient information extraction, misjudgment often occurs, and the cloud boundary processing is also very rough. In order to improve the accuracy of cloud and cloud shadow segmentation, we propose a multilevel feature context semantic fusion network. The network takes the residual network as the backbone networkand adopts the structure of encoding and decoding as a whole. In the model, we introduce the multibranch residual context semantic module, the multiscale convolution subchannel attention module, and the feature fusion upsampling module to strengthen the feature extraction, refine the cloud and cloud shadow edge information, and enhance the actual segmentation ability of the model. The experimental results show that the proposed method is more accurate than the previous network in the segmentation of cloud and cloud shadow and has good generalization ability on other datasets, which is of great significance for the study of cloud.
The use of remote sensing images for land cover analysis has broad prospects. At present, the resolution of aerial remote sensing images is getting higher and higher, and the span of time and space is getting larger and larger, therefore segmenting target objects enconter great difficulties. Convolutional neural networks are widely used in many image semantic segmentation tasks, but existing models often use simple accumulation of various convolutional layers or the direct stacking of interfeature reuse of up- and downsampling, the network very heavy. To improve the accuracy of land cover segmentation, we propose a multichannel feature fusion lozenge network. The multichannel feature fusion lozenge network (MLNet) is a three-sided network composed of three branches: one branch uses different levels of feature indexes to sample to maintain the integrity of high-frequency information; one branch focuses on contextual information and strengthens the compatibility of information within and between classes; and the last branch uses feature integration to filter redundant information based on multiresolution segmentation to extract key features. Compared with FCN, UNet, PSP, and other serial single road computing models, the MLNet, which performs feature fusion after three-way parallelism structure, can significantly improve the accuracy with only small increase in complexity. Experimental results show that the average accuracy of 85.30% is obtained on the land cover data set, which is much higher than that of 82.98% of FCN, 81.87% of UNet, 77.52% of SegNet, and 83.09% of EspNet, which proves the effectiveness of the model.
In remote sensing images, the complex and changeable background often interferes with the detection of cloud and its shadow, which leads to the phenomenon of missing detection in areas with similar background colors. In addition, most methods cannot achieve the real-time detection effect because of too much calculation. To solve the above problems, the parallel asymmetric network with double attention is proposed. The algorithm adopts a parallel method, which allows two branches to participate in the calculation at the same time. One branch is used to fuse different information from two branches at different levels, and another branch is responsible for extracting deeper context information. At the same time, double attention module and asymmetric dilated block are used for two branches, respectively. Double attention module can help the algorithm pay more attention to the category and spatial information of cloud and its shadow, thus reducing the interference caused by background information in images. Asymmetric dilated block can extract two levels of receptive fields, and it can help the network to obtain enough receptive fields in cloud and its shadow images, thus reducing the cases of missed detection and false detection. Moreover, these two modules are lightweight in their parameters and calculation. Compared with some previous methods, our method can guarantee the accuracy and the detection speed is fast.
The detection of urban land use is of great significance to urban planning. With the development of deep learning technology, convolutional neural networks (CNN) are widely used in remote sensing image processing. However, existing CNN models lack the ability to fuse high-dimensional semantic information and low-dimensional position information of images and thus perform poorly in feature restoration. To obtain better urban land use detection results, the cross-dimensional feature fusion network (CDFFNet) is proposed in this work. In the CDFFNet, Class Feature Attention (CFA), cross-level feature fusion (CLFF), and Same Level Feature Gate Fusion (SLFGF) are designed. The CFA aggregates multi-scale and multi-receptive field input features to capture high-dimensional semantic information. The CLFF is designed to fuse features of different resolutions. It can extract and fuse semantic information of different dimensions, aggregate contextual information of features and distinguish the contextual relevance of each pixel. The SLFGF dynamically fuses high-dimensional semantic information and low-dimensional spatial information, so it could enhance information recovery of different categories. Compared with existing methods, the CDFFNet is significantly improved in accuracy. Its mean intersection over union on the Aerial Image Segmentation Dataset reaches 0.765.
In recent years, the rapid development of Internet technology and the advent of information age, people are increasing the strong demand for the information products and the market for information technology. Particularly, the network security requirements have become more sophisticated. This paper analyzes the wireless network in the data security vulnerabilities. And a list of wireless networks in the framework is the serious defects with the related problems. It has proposed the virtual private network technology and wireless network security defense structure; and it also given the wireless networks and related network intrusion detection model for the detection strategies.
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