Over the past decade, jamming methods for Synthetic Aperture Radar (SAR) target detection and recognition, such as generating false targets, electromagnetic (EM) deception, and spoofing, have garnered significant atte...
详细信息
ISBN:
(纸本)9798350360332;9798350360325
Over the past decade, jamming methods for Synthetic Aperture Radar (SAR) target detection and recognition, such as generating false targets, electromagnetic (EM) deception, and spoofing, have garnered significant attention. Distinguished from conventional SAR jamming methods, the target jamming scenario for SAR based on a new man-made material cloak introduces enhanced possibilities. These technologies exhibit the capability to effectively scatter incident EM waves in arbitrary or desired directions, thereby concealing pertinent information associated with the critical target. In this paper, we propose a broadband target jammer of SAR based on an information metasurface, which integrates intelligent information processing algorithms with space-time-coding digital metasurface, providing the capability to manipulate incident EM waves freely to achieve multi-mode jamming protections for critical targets. In simulation experiments, the results demonstrate adjustable EM deception and the generation of multiple false targets without any cooperation with the SAR system. Our work brings the available protection strategies for SAR closer to a wide range of real-time, broadband, and controllable applications, enhancing the concealment of critical targets in hot conflict zones.
remotesensing is of great importance for analyzing and studying various phenomena occurrence and development on *** is possible to extract features specific to various fields of application with the application of mo...
详细信息
remotesensing is of great importance for analyzing and studying various phenomena occurrence and development on *** is possible to extract features specific to various fields of application with the application of modern machine learning techniques,such as Convolutional Neural Networks(CNN)on MultiSpectral images(MSI).This systematic review examines the application of 1D-,2D-,3D-,and 4D-CNNs to MSI,following Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA)*** review addresses three Research Questions(RQ):RQ1:“In which application domains different CNN models have been successfully applied for processing MSI data?”,RQ2:“What are the commonly utilized MSI datasets for training CNN models in the context of processing multispectral satellite imagery?”,and RQ3:“How does the degree of CNN complexity impact the performance of classification,regression or segmentation tasks for multispectral satellite imagery?”.Publications are selected from three databases,Web of Science,IEEE Xplore,and *** on the obtained results,the main conclusions are:(1)The majority of studies are applied in the field of agriculture and are using Sentinel-2 satellite data;(2)Publications implementing 1D-,2D-,and 3D-CNNs mostly utilize *** 4D-CNN,there are limited number of studies,and all of them use segmentation;(3)This study shows that 2D-CNNs prevail in all application domains,but 3D-CNNs prove to be better for spatio-temporal patternrecognition,more specifically in agricultural and environmental monitoring applications.1D-CNNs are less common compared to 2D-CNNs and 3D-CNNs,but they show good performance in spectral analysis tasks.4D-CNNs are more complex and still underutilized,but they have potential for complex data *** details about metrics according to each CNN are provided in the text and supplementary files,offering a comprehensive overview of the evaluation metrics for each type of machine learning technique
Accurate classification and identification of vessels in remotesensing satellite imagery is critical for ocean monitoring and resource management. The ability to extract information from remote-sensing data is of par...
详细信息
ISBN:
(纸本)9798350350920
Accurate classification and identification of vessels in remotesensing satellite imagery is critical for ocean monitoring and resource management. The ability to extract information from remote-sensing data is of paramount importance. To exploit the non-stationary characteristics of synthetic aperture radar (SAR) target, a comprehensive SAR ship recognition framework is designed by combing the second-order synchrosqueezing transform (SST), an effective non-stationary signal processing tool, with the histogram of oriented gradient (HOG) feature in this paper. Firstly, the second-order SST is performed on SAR images to describe the non-stationary characteristics of ships at different times and frequencies. Secondly, HOG features are utilized to effectively extract the non-stationary information of SAR ships and provide more discriminative input for the deep learning network. Then, the optimal ResNet model is selected as the convolutional neural network (CNN) classifier to automatically fuse the non-stationary features and abstract features of SAR ships. Experiments on two open SAR ship datasets (OpenSARShip and FUSAR-Ship) show that the proposed method achieves accurate classification and outperforms the state-of-the-art (SOTA) CNN-based methods in terms of robustness and generalization ability. The positive effect of non-stationary characteristics on SAR ship classification is verified.
The image classification as the key technology in the remotesensing system is mainly based on the image with the characteristics of the electromagnetic wave radiation information of the ground object to distinguish a...
详细信息
In order to explore the feasibility of applying UAV remotesensing and object-oriented canopy extraction technology to forest areas with different canopy densities, 26 forest plots located in Gannan Plateau were selec...
详细信息
Accurate sensing of contamination on the insulator surface is vital for the reliable operation of transmission lines. Hence, the present work aims to develop a deep learning framework for remote and accurate contamina...
详细信息
Accurate sensing of contamination on the insulator surface is vital for the reliable operation of transmission lines. Hence, the present work aims to develop a deep learning framework for remote and accurate contamination sensing on the surface of outdoor insulators. The experiment is conducted on an 11-kV porcelain disc insulator to generate an extensive database of images representing different insulator surface conditions, i.e., clean surface and surface with sand, mud, and ash contaminations. The captured images were fed to a customized convolutional neural network (CNN) architecture for automated feature extraction and recognition. The proposed CNN model delivers appreciably high recognition accuracy at significantly reduced training time for sensing various insulator contaminations compared with other benchmark CNN models (AlexNet, VGGNet16, and ResNet50). Moreover, the proposed framework delivers an excellent recognition performance in sensing surface contamination under different lighting conditions. Therefore, the proposed methodology can be implemented for the condition monitoring of real-life insulators.
The point source target (PST) can provide high object and image positioning accuracy and is expected to play an important role in the precise geometric processing of optical remotesensing sensors in the future. This ...
详细信息
ISBN:
(数字)9783031024443
ISBN:
(纸本)9783031024443;9783031024436
The point source target (PST) can provide high object and image positioning accuracy and is expected to play an important role in the precise geometric processing of optical remotesensing sensors in the future. This paper proposes a method for intelligently recognizing PST image control points (ICPs) from satellite imagery, which can improve the intelligent level of geometric processing of optical remotesensing sensors. Two deep convolutional neural networks (DCNNs), Faster R-CNN and CenterNet are selected to complete the recognition task. Due to the lack of training data, a large number of simulated samples are generated considering the PST image characteristics. The simulated and real PST ICPs are then used to test the trained DCNNs. The two DCNNs complete the recognition task on the simulated dataset successfully. The Recall and Precision values of the two DCNNs are close to 100%. The performance of the two DCNNs on real PST ICPs is worse than that on the simulated data, but the recognition task is also well completed when the quality of PST ICPs is good. The Recall values of both models are above 95%, and the Precision values are close to 100%. Experiment results also show that the performance of CenterNet is better than Faster R-CNN and the image quality has a great impact on the recognition performance.
Since the temporal variation for growing period of paddy rice can be shown clearly in optical and radar images, a Long-Short Term Memory (LSTM) model is introduced to construct paddy rice recognition systems using Sen...
详细信息
The reconstruction of motion blur is a significant subject in remotesensingimageprocessing. It has a great effect on the follow-up processes of target detection and recognition. To meet the needs of on-board intell...
详细信息
Cloud and snow in remotesensingimages typically block the underlying surface information and interfere with the extraction of available information, so detecting cloud and snow becomes a critical problem in remotely...
详细信息
Cloud and snow in remotesensingimages typically block the underlying surface information and interfere with the extraction of available information, so detecting cloud and snow becomes a critical problem in remotely sensed imageprocessing. The current methods for detecting clouds and snow are susceptible to interference from complex background, making it difficult to recover cloud edge details and causing missing and false detection phenomena. To address these issues, a cross-dimensional feature attention aggregation network is suggested to realize the segmentation of clouds and snow. To address the problem of interference induced by the similar spectral characteristics of clouds and snow, the context attention aggregation module is added to conflate feature maps of various dimensions and screen the information. Multi-scale strip convolution module (MSSCM) and its improved version MSSCMs are used to extract edge characteristics at different scales and improve the harsh segmentation border. Also, adding deep feature semantic information extraction module to deep features to guide the classification of the model to avoid the interference of complex background. Finally, a 'los beatles' module is used to replace the traditional linear combination in the decoding stage, and the feature information of different granularity is fused and extracted to enhance the model's detection efficiency. In this paper, experiments are carried out on the public datasets: CSWV, HRC_\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\_$$\end{document}WHU and L8_\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\_$$\end{document}SPARCS
暂无评论