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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing IX"

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remote sensing Vehicle Detection Based on Embedded System 12

Remote Sensing Vehicle Detection Based on Embedded System

引用

12th International conference on signal processing Systems

作者： Su, Haoxiang Dong, Zhenghong Yang, Fan Lin, Yu Space Engn Univ 1 Bayi Rd Beijing Peoples R China

ISBN: (纸本)9781510642768;9781510642751

At present. remote sensing image vehicle detection based on deep learning has achieved certain results. but most of them rely on powerful PC computing power and cannot be deployed in satellites, so they cannot provide support for satellite in-orbit detection. Aiming at this problem, this paper proposes a remote sensing image vehicle detection method based on YOLOv5 model and successfully deploys it in Jetson TX2 embedded equipment that can be deployed on a satellite platform. Experiments have proved that the algorithm proposed in this article detects vehicle targets in a 12000* 12000 pixels wide remote sensing image in an embedded device, and the detection time is only about 1 minute and 20 seconds at the fastest.

关键词： remote sensing vehicle detection YOLOv5 embedded device

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Style Adaptation and Deformable Convolution for Change Detection Networks

Style Adaptation and Deformable Convolution for Change Detec...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Sun Heran Electronic Information College Northwestern Polytechnical University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

remote sensing image change detection plays an important role in the fields of urban change detection, environmental protection and geologic hazard identification. Change targets are mostly irregular shapes, and it is a challenge to extract the change characteristics of remote sensing images using ordinary convolutional methods because the perceptual field of convolution is fixed. In this paper, a style adaptation and deformable convolution network(SDNet) is proposed and use a deformable convolutional network with residual links in SDNet. Global context and change characteristics details can be captured in a much shallower way. In addition, a special detection module is proposed for comparing multilevel features extracted from two branches, where the features of each branch are shared. In order to reduce the background and illumination of the diachronic images, a style adaptation technique is used to make the two images similar in style. The effectiveness of our proposed approach has been validated through experiments conducted on two public datasets.

关键词： Convolution Shape image edge detection Geology Lighting Feature extraction Hazards Convolutional neural networks Protection remote sensing

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Secure 'text in image' steganography scheme based on Alexnet and contour-let transform 2

Secure 'text in image' steganography scheme based on Alexnet...

引用

2nd International conference on Artificial Intelligence and signal processing, AISP 2022

作者： Srinivasu, Lingamallu Naga Veeramani, Vijayaraghavan Department of Electronics and Communication Engineering Vignan'sFoundation for Science Technolog and Research Vadlamudi Andhra Pradesh Guntur522213 India

ISBN: (纸本)9781665442909

Nowadays, 'text in image' steganography is utilised in a variety of applications like military, surveillance, and remote sensing etc., in order to keep the secret information secure. This paper is presenting the unique steganography algorithm for improving data security, visual quality, quality metrics and withstand the attacks of the stego image. In proposed algorithm, the security of the confidential information is improved by utilizing the SLICE encryption algorithm. It is used to generate the cipher data from the confidential data. The alexnet is introduced in this paper to detect the facial area in human cover image. A total of 1987 images are used to train the network and got maximum accuracy. Contour-let transform is utilized to decomposition of alexnet output. The "random pixel embedding" (RPE) technique is utilized to embed the confidential data in facial area of the sub-band. The combination of alexnet and contour-let transform is used to generate good visual quality of the stego image. The proposed algorithm produces a stego image that has better visual quality, security, metric-values and withstand attacks compared to recent methods. © 2022 IEEE.

关键词： Cryptography

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引用

IET International Radar conference 2023, IRC 2023

作者： Zuo, Yixin Li, Guangzuo Ren, Wenjuan Research Department of Cyber-electromagnetic Space Information Technology Chinese Academy of Sciences Beijing100094 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China

ISBN: (纸本)9781839539954

Polarimetric SAR (PolSAR) image classification is a critical application of remote sensing image interpretation. Most of the early algorithms have a general classification performance, which use hand-crafted features to divide the image into various scattering categories. The Convolutional Neural Networks (CNNs) show superior performance in image processing with powerful nonlinear representation capabilities. However, they require a large amount of labeled training data, which limits the practical use of CNNs in PolSAR image classification scene where labeled data is rare and expensive. To address the previous issue, this paper proposes a deep similarity-based unsupervised classification network with compound regularization. The classification network combines a pre-trained feature extraction pipeline with convolutional autoencoder (CAE) and an unsupervised classification pipeline with deep feature similarity measurement. The regularization combines two ingredients to preserve both the sharpness of edges and the semantic continuity of the image contents. The first is the differential constraint based on pixel-level features, and the second is the graph model constraint based on superpixel-level features. Experiments prove the effectiveness of the proposed method on both spaceborne (RadarSat2) and airborne (E-SAR) PolSAR images. The visual results and quantitative scores show that our method outperforms the previous unsupervised classification methods with a large margin. © The Institution of Engineering & Technology 2023.

关键词： Convolutional neural networks

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Hybrid Deep Learning for Multi-Modal image Fusion: Elevating Brain Tumor Detection 2

Hybrid Deep Learning for Multi-Modal Image Fusion: Elevating...

引用

2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Mehta, Shiva Singh, Amanveer Chitkara University Institute of Engineering and Technology Centre for Research Impact & Outcome Punjab Rajpura140401 India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India

ISBN: (纸本)9798350366570

This work presents a new dual deep learning framework that incorporates CNNs and ViTs into the multi-modal medical picture fusion to enhance the diagnostic accuracy of the diagnosed brain tumour. This integration concerns MRI and PET information, applying the ability of CNNs to handle local spatial information and ViTs in collecting long-range relationships. Here, to test the efficacy of the proposed model, a publicly available multi-modal brain tumour database was used, where significant performance improvements were achieved over the conventional techniques. The developed model achieved a sensitivity of 96.8%, while the traditional CNN-based methods attained a sensitivity of only 91.2%. In addition, the research used it to achieve a high accuracy rate of 95.7 percent, a recall rate of 95.1 percent, and an F1 score of 95.4 percent for infallibility in locating tumors with minimal false positives and negatives. It was realized that the model in this study provided improved picture quality, with an SSIM of 0.89 compared to the 0.82 of earlier fusion techniques. The pilot qualitative study confirmed that the work of the fused pictures showed a better combination of PET and MRI information by merging spatial features with metabolic data to help distinguish the tumour's boundaries more clearly and identify small or irregular form tumours. The proposed CNN-ViT model outperforms other methods for detecting tumors with high levels of accuracy and high-quality images that give medicine a vital tool. The methodology has high potential to be applied to other medical imaging jobs as well. © 2024 IEEE.

关键词： Medical imaging

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K2NN: Self-Supervised Learning with Hierarchical Nearest Neighbors for remote sensing

K2NN: Self-Supervised Learning with Hierarchical Nearest Nei...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jianlong Yuan Yuanhong Xu Zhibin Wang Alibaba Group

Self-supervised learning aims to learn applicable pre-trained models from massive unlabeled data. Besides image-level pretext tasks, many recent pixel-level studies have been pro-posed to learn dense information in each image. However, most of those methods focus on obtaining pair of matched patches from the same image with different augmentation. At the same time, little effort is devoted to exploiting matched patches from different images. In this work, we develop a novel pixel-level task that leverages an ensemble of nearest neighbors from multiple images to explore diverse objects in each image, especially for remote sensing data. Besides, a sampling strategy with a submodular function is adopted to efficiently update the memory bank consisting of patches. The extensive experiments on remote sensing data confirm the effectiveness of our method.

关键词： Memory management Self-supervised learning signal processing Data models Acoustics Sensors Task analysis

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Range Imaging of Integrated Radar Communication signal Based on OFDM

Range Imaging of Integrated Radar Communication Signal Based...

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2022 International conference on Information processing and Network Provisioning, ICIPNP 2022

作者： Chen, Zhivu Zhao, Jingcheng Hong, Haohui Chen, Zhihua Cheriet, Mohamed Beihang University Beijing China Yunnan Innovation Institute • BUAA Kunming China Beijing Institute of Remote Sensing Equipment Beijing China Southwest Technology and Engineering Research Institute Chongqing China MontrealQC Canada

ISBN: (纸本)9781665464055

First, based on the radar echo model of OFDM integrated radar communication signal, this article investigates the impact of target velocity and distance on the delay phase and Doppler frequency offset phase in radar echo signal by using the estimation method of Doppler frequency offset. Then, through formula derivation, the imaging principle of radar one-dimensional range image based on OFDM integrated radar communication signal is obtained, and MATLAB is used to simulate the multi-target radar one-dimensional range image of OFDM integrated signal. Finally numerical results verify that OFDM-based radar communication integrated signal can meet the design requirements of range resolution of 0.5m, and has more optimized resolution and imaging accuracy. © 2022 IEEE.

关键词： Orthogonal frequency division multiplexing

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Diffusion posterior sampling for remote sensing image fusion

Diffusion posterior sampling for remote sensing image fusion

引用

7th International conference on Vision, image and signal processing (ICVISP 2023)

作者： C. Zhang Z. Wang J. Han College of science National University of Defense Technology Changsha People's Republic of China

remote sensing image fusion, i.e., fusing remote sensing images from different sensors or different time into a comprehensive image, can integrate image information for all kinds of image tasks, such as object detection, pattern recognition, and so on. In this paper, we focus on the image fusion of optical image and synthetic aperture radar (SAR) image, where the traditional methods, such as sparse representation and image decomposition, usually fail to improve the image quality and enhance object features' level. Inspired by the powerful generation model named diffusion denoising probabilistic model (DDPM), we propose a novel method based on diffusion posterior sampling for image fusion of optical image and SAR image. Starting from the variational model of image fusion and the total variation constraint, we approximate the posterior sampling process of image fusion by a closed-form analytic solution. After that, DDPM can be used to generate the fused image with high image quality and enhanced object features. The feasibility and the superiority of the proposed method is validated in numerical experiments.

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Development of the information-logical scheme for Earth remote sensing small spacecraft

Development of the information-logical scheme for Earth remo...

引用

Information Technology and Nanotechnology (ITNT), International conference on

作者： Sergey Safronov Maksim Ivanushkin Maksim Korovin Ivan Kaurov Ivan Tkachenko Anastasiia Krestina Samara National Research University Samara Russian Federation Image Processing Systems Institute RAS -Branch of the FSRC "Crystallography and Photonics" RAS Samara National Research University Samara Russian Federation

The paper considers a modern approach to the design process of the Earth remote sensing small spacecraft using information technologies. The onboard composition is considered and a block diagram of the Earth remote sensing small spacecraft is developed in order to develop an information-logical diagram of internal interaction. The developed scheme allows, already at the design stage, by setting the necessary characteristics, to quickly form the onboard composition of any spacecraft, taking into account the efficiency of the joint operation of certain devices, as well as significantly reduce the time for design work on the development of small spacecraft.

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Super-Resolution-Based Change Detection Network with Self-attention for images with Different Resolutions 20th

Super-Resolution-Based Change Detection Network with Self-at...

引用

20th International conference on Intelligent Computing (ICIC)

作者： Wang, Shaopeng Han, Meng Niu, Chaoyu Hou, Min Inner Mongolia Univ Coll Comp Sci Hohhot 010021 Inner Mongolia Peoples R China

ISBN: (纸本)9789819756742;9789819756759

SRCDNet is currently the only model utilizing deep learning methods for change detection in two temporal images with different resolutions. However, the SR module of this model exhibits defects such as pseudo-detail and inaccurate texture processing, and there are shortcomings in the extraction of target difference features. To address these issues, this paper proposed SA-SRCDNet, this model incorporates the self-attention mechanism into the original SR module, enabling it to capture more global dependencies and deepen the network during the resolution conversion process. In addition, the Batch Normalization (BN) layers in the convolutional layers of the generator within the SR module were removed to preserve the subtle details and texture information of the images. The Charbonnier loss was employed to replace the Mean Squared Error (MSE) loss, aiming to better handle outliers during the super-resolution restoration process. Multiple sets of experiments conducted on public change detection datasets, CDD and BCDD, demonstrate that SA-SRCDNet model achieves superior Peak signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metric values on the image test set. The denoising effect is evident, showcasing excellent pseudo-detail processing capability. Additionally, The experimental results of SA-SRCDNet on the CDD(x4, x8) dataset demonstrate that it's IoU and F1 values reach up to 83.60%, 91.06%, and 72.62%, 84.14%, respectively.

关键词： super-resolution remote sensing imagery change detection deep learning self-attention mechanism

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