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

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Active-Passive Radar for Radar Imaging from Aerospace Carriers 19

Active-Passive Radar for Radar Imaging from Aerospace Carrie...

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19th International conference on Smart Technologies (IEEE EUROCON)

作者： Pavlikov, Volodymyr Volosyuk, Valeriy Zhyla, Simeon Tserne, Eduard Shmatko, Olexandr Sobkolov, Anton NAU Kharkiv Aviat Inst Aerosp Radioelect Syst Dept Kharkiv Ukraine

ISBN: (纸本)9781665432993

Algorithms for radar imaging of the underlying surface from aerospace carriers have been synthesized. In doing so, we have considered two possible ways of image reception based on methods of active and passive remote sensing. On this basis, a radar complex, which allows forming images proportional to either the specific effective surface scattering or the effective noise temperature of the underlying surface, has been created. The structural scheme of the complex in accordance with both operating modes is developed. The results of radio image simulation are given.

关键词： radar imaging signal processing algorithm image filtration ultrawideband signals

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CFAR-CNN Detector of Ships from SAR image Using Generalized Gamma Distribution and Real Dataset

CFAR-CNN Detector of Ships from SAR Image Using Generalized ...

引用

International conference on image and signal processing and their Applications (ISPA)

作者： Souad Chabbi Faiç al Farah Nouh Guidoum Laboratoire Signaux et Syst&#x00E8 mes de Communication (SISCOM) Universit&#x00E9 des Fr&#x00E8 res Mentouri Constantine Algeria

The detection and localization of ships is important and must be accurate and rapid. The Synthetic Aperture Radar (SAR) is optimal for ship detection. Generally, Constant False Alarm Rate (CFAR) algorithms are used to detect ships from a SAR image on the base of satellite remote sensing imaging. However, due to the rapid development of technology, the remote sensing data have shown the features of big-data. The analyze of big-data improves the accuracy and speed of the ship detection. Therefore, Deep Learning (DL) is recommended and exactly the Convolutional Neural Network (CNN) model has greatly improved the static image recognition performance. In this paper, to improve the accuracy and speed of the ship detection from SAR image, we introduce the CFAR-CNN detector. First, after modeling the sea clutter by the Generalized Gamma (GΓ) distribution, a CFAR global detector is applied. Then, to improve the accuracy of the previous results, a CNN local detector is applied. To this end, a real dataset is used to obtain the optimal CNN model. We have shown that this detector is rapid and tends toward ideality.

关键词： Satellites Detectors signal processing Radar imaging Radar polarimetry Convolutional neural networks Marine vehicles

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2nd International conference on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning, 3DIT-MSPandDL 2020

2nd International Conference on 3D Imaging Technologies-Mult...

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2nd International conference on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning, 3DIT-MSPandDL 2020

ISBN: (纸本)9789811633904

The proceedings contain 39 papers. The special focus in this conference is on 3D Imaging Technologies-Multidimensional signal processing and Deep Learning. The topics include: Appearance Defect Detection of Injection Parts Based on Deep Learning;thresholding Method for the Sonar image of a Seabed Target Based on Two-Dimensional Renyi's Entropy;noise Reduction in Sonar images of Seabed Targets Based on a Variational Method;sonar image Segmentation of Seabed Targets Using a Variational Approach;balance the Robustness and Invisibility of Digital Watermarking by image Entropy in Multiple Domains;research Progress of Electrical Resistance Tomography;research on Low-Resolution image Matching and Recognition Algorithm Based on Unified Feature Space;research on Realistic Representation Technology in Virtual Environment;intelligent Customer Service Operation Management System Solution Based on Intelligent Voice Analysis;Research Status of Motor imagery EEG signal Based on Deep Learning;research on Car Seat Comfort Evaluation System Based on Chinese Population;research on Building Extraction from High-Resolution remote sensing image Based on Improved U-Net;a Survey of image Classification Algorithms Based on Graph Neural Networks;an Improved Method of Non-local Means Denoising Based on Histogram;short-Term Traffic Volume Prediction Based on Gray Wolf Optimization Algorithm;Biological Particle Recognition Based on BP Neural Network Algorithm;imageCube: A Low-Cost 6D Controller with Smooth Tracking for Mobile Augmented Reality;A Clinical Decision-Making System for COVID-19;License Plate Recognition System Based on OpenCV;LUCC and Landscape Pattern Variation in the Lower Tarim River by remote sensing;image Recognition Based on Super-Resolution Wasserstein Generative Adversarial Nets with Gradient Penalty.

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Crop Classification from Multi-Temporal and Multi-spectral remote sensing images 29

Crop Classification from Multi-Temporal and Multi-spectral R...

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29th IEEE conference on signal processing and Communications Applications (SIU)

作者： Kizilirmak, Firat Aptoula, Erchan Sabanci Univ Bilgisayar Bilimi & Muhendisligi Istanbul Turkey Gebze Tekn Univ Teknol Enstitusu Kocaeli Turkey

ISBN: (纸本)9781665436496

The number of satellites, equipped with various sensors, aiming to observe agricultural activities have been progressively increasing. Satellite technology advances have enabled the acquisition of multispectral images of a region with small temporal intervals. Consequently, changes over a region can be observed, yield forecast can be made and the type of crops can be determined. In this work, it is aimed to classify 13 different crops by processing the multi temporal and multispectral data consisting of surface reflectance values. To this end, a siamese recurrent neural network structure, that processes time series information with deep metric learning approaches and providing easier classification, is proposed. A convolutional neural network that processes the multi temporal and multispectral information like an image is proposed to reduce the effect of class imbalance problem. These models are then combined under an ensemble neural network structure in order to leverage both networks' strengths. The proposed method outperforms other studies on the literature on BreizhCrops dataset.

关键词： Deep metric learning Recurrent neural network Convolutional neural network Ensemble neural network

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Fast Thermal Infrared image Ground Object Detection Method based on Deep Learning Algorithm 6

Fast Thermal Infrared Image Ground Object Detection Method b...

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6th International conference on Communication, image and signal processings, CCISP 2021

作者： Zhou, Guopeng Wu, Falin He, Jiaqi Li, Haolun Zhang, Chunxiao Yang, Gongliu Beihang University Beijing China Beijing Institute of Space Mechanics & Electricity Beijing China

ISBN: (纸本)9781665432795

Nowadays, there is an increasing demand for wild searching and nature reserve monitoring in nighttime environment. Combined with the advantages of thermal infrared (TIR) imaging technology and the flexibility of unmanned aerial vehicle (UAV), the UAV thermal infrared remote sensing can provide sufficient image data as the information source of decision-making and judgment for multi scene object detection task at night. In order to process these image data quickly and accurately, and to realize the purpose of ground object detection, this paper investigates a fast thermal infrared image ground object detection method based on deep learning algorithm. The proposed method achieves good accuracy and speed performance on the dataset, which shows 64.35% mAP with 41.83 FPS. This method is superior to the other general detection algorithms in detection accuracy and detection speed. © 2021 IEEE

关键词： Object detection

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Extraction of Waterbody Using Object-Based image Analysis and XGBoost 3rd

Extraction of Waterbody Using Object-Based Image Analysis an...

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3rd International conference on Machine Intelligence and signal processing, MISP 2021

作者： Chatufale, Aditya P. Rege, Priti P. Bhatt, Abhishek College of Engineering Pune India

ISBN: (纸本)9789811908392

Water, as an important resource, is critical to sensible development. For the benefits of the climate and humanity, the scenario with water assets needs to be assessed precisely. However, it is miles exceptionally troublesome, tedious, and quite hard to assess a massive water area using conventional ground survey strategies. remote sensing for quite a while has been applied in water asset evaluation. The present-day techniques depending on water indices, viz., the water ratio index (WRI), the normalized difference vegetation index, the normalized difference water index, the modified normalized difference water index are typically carried out for water resource assessment, which delineates the waterbody through utilizing predefined threshold value. Generally, threshold selection is difficult due to the presence of environmental noise, which includes vegetation, shadow, cloud, and built-up land, prompting misclassification. In this experimental study, a new technique for water body extraction using object-based image analysis (OBIA) and deep learning approach is put forward to lessen the errors. The trial outcomes display that for the proposed strategies, overall mapping accuracy is better than the ones made using the water indices. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Extraction

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2021 6th International conference on signal and image processing, ICSIP 2021

2021 6th International Conference on Signal and Image Proces...

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6th International conference on signal and image processing, ICSIP 2021

ISBN: (纸本)9780738133737

The proceedings contain 239 papers. The topics discussed include: single-channel speech enhancement using multi-task learning and attention mechanism;KL divergence based objective state selection method for EW conflict;semi-supervised anomaly detection and location based on generative adversarial network;high resolution imaging of GEO SAR under the influence of terrain elevation;research on hand soft rehabilitation system based on brain-computer interface and virtual reality;use of depth completion and SLAM algorithm to build dense maps of large scenes;efficient FFT based multi-source DOA estimation for ULA;Pivot-V: an optimized algorithm for password generation in PCFGs model;a co-simulation method of polarization imaging for temporary birefringent materials;and a two-stage line matching method for multi-temporal remote sensing images.

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Hyperspectral image classification using spectral-spatial hypergraph convolution neural network 27

Hyperspectral image classification using spectral-spatial hy...

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conference on image and signal processing for remote sensing XXVII

作者： Ma, Zhongtian Jiang, Zhiguo Zhang, Haopeng Beihang Univ Sch Astronaut Dept Aerosp Informat Engn Beijing 102206 Peoples R China Beijing Key Lab Digital Media Beijing 102206 Peoples R China

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

Deep learning methods, especially convolutional neural networks(CNN), have been widely used in hyperspectral image(HSI) classification. Recently, graph convolutional networks (GCN) have shown great potential in HSI classification problem. However, the existing GCN-based methods have several problems. First, the existing methods rely too much on the adjacency matrix, which cannot be changed during training. Furthermore, most of them can only use a single kind of feature, and fail to extract the spectral-spatial information from the HSI. Finally, for the existing GCN-based methods, it is difficult to achieve the same accuracy as the mature CNN methods. In this paper, we propose a spectral-spatial hypergraph convolutional neural network ((SHCN)-H-2) for HSI classification. Compared with the existing GCN-based methods, (SHCN)-H-2 has the following advantages. Different from the adjacency matrix that is fixed during training of GCN, (SHCN)-H-2 can dynamically update the weight of the hyperedge during training, which reduces the reliance on prior information to a certain extent. In addition, (SHCN)-H-2 generates hyperedges from the spectral and spatial features independently, and adopts the incidence matrix composed of all hyperedges to replace the adjacency matrix in GCN. In this way, the spectral and spatial features can be better integrated. Finally, compared to a simple graph structure, the hypergraph structure can express the high-dimensional relationships in the data, which is beneficial to classification problems. Sufficient experiments on two popular HSI datasets have proved the effectiveness of (SHCN)-H-2.

关键词： Graph convolution networks hypergraph learning hyperspectral image (HSI) classification feature fusion deep learning

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Lightweight YOLOv4 Algorithm for remote sensing image Detection

Lightweight YOLOv4 Algorithm for Remote Sensing Image Detect...

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International conference on signal processing Systems, ICSPS

作者： Li Ma Tong He Yong Sun Bin-Bin Hu College of Information Liaoning University Shenyang China

remote sensing images have the characteristics of complex backgrounds, high resolution, and small targets. Although the existing object detection algorithms can improve the detection accuracy, there are generally problems such as a large number of model parameters, high computational cost, and poor real-time performance. Aiming at the above problems, this paper designs a lightweight object detection algorithm GSC-YOLO based on YOLOv4 to achieve fast and accurate detection of remote sensing images. First, Ghostnet is used as the feature extraction network of GSC-YOLO to reduce the number of parameters and improve the detection speed; Secondly, the improved shuffle attention mechanism is introduced in the prediction head to make the model pay attention to important information and improve the detection accuracy; Finally, the Confidence Propagation Cluster algorithm CP-Cluster is used to post-process the prediction frame to improve the object recognition. Taking the preprocessed DOTA dataset as the experimental object, the experimental results show that the GSC-YOLO algorithm has a detection accuracy of 93.44%, a detection speed of 58 frames per second, and a model size of 43.65MB. Compared with the remote sensing image object detection algorithm based on YOLOv4, the detection accuracy is increased by 3.93%, the detection speed is increased by 1.87 times, and the model size is reduced by 5.62 times, which is more suitable for deployment on devices with limited resources.

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Application of Geostationary Satellite Based Range-Gated Lidar Imaging in Weak Target Detection 12th

Application of Geostationary Satellite Based Range-Gated Lid...

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12th International conference on signal and Information processing, Networking and Computers, ICSINC 2024

作者： Chen, Zhuoyi Sun, Hezhi Kong, Xianghao Zhao, Zhiwei Wang, Chenglun China Academy of Space Technology Beijing100094 China

ISBN: (纸本)9789819649662

An active lidar detector for range-gated imaging is employed on geostationary satellites to suppress the image noise caused by backscattered light by controlling the on and off times of the receiving system cameras. This approach ensures that the imaging is less affected by environmental factors for distant targets. For the active illumination light pulse, although the instantaneous energy is high, the pulse is narrow, the average power is low, and the overall energy consumption and weight of the system can be well controlled. This is an effective method that is especially suitable for geostationary satellites to perform ground and sea detection. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： remote sensing

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