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2023 International Conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Liu, Ying Nanjing Vocational Institute of Transport Technology China

ISBN: (纸本)9781510666351

This paper addressed the contradiction between the decrease of labor force and the increase of agricultural output demand in agricultural development, combined with the technical advantages of UAV imaging radar in SAR imaging, ground moving target detection and height measurement, analyzed the key technology of UAV imaging radar applied to agricultural plant protection. The signal processing scheme and processing process of UAV imaging radar for agricultural plant protection were designed, and the experimental verification results using the measured data were reasonable and feasible. The verification results of the measured data showed that the UAV remote sensing imaging technology can realize the high-resolution imaging and preliminary ground object classification in the operation area. © 2023 SPIE.

关键词： Radar imaging

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Characterisation of Resident Space Objects and Synchronisation Error Compensation in Multistatic Interferometric Inverse Synthetic Aperture Radar Imaging

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IET RADAR SONAR AND NAVIGATION 2025年第1期19卷

作者： Rudrappa, Manjunath Thindlu Kaeske, Martin Albrecht, Marcus Knott, Peter Fraunhofer Inst High Frequency Phys & Radar Tech F Wachtberg Germany

This article presents a study on interferometric inverse synthetic aperture radar (ISAR) for three-dimensional imaging and rotational velocity estimation. The study focuses on synchronisation error compensation in a multistatic setup with three ground-based radars and non-orthogonal baselines. The simulation involves CAD models of non-cooperative targets in Keplerian orbits with different orbital parameters, and radar backscattering along the orbit is simulated based on physical optics approximation. The paper also illustrates the signal processing chain for synchronisation error compensation and the generation of interferometric pointclouds for the resident space object models in orbit. The study includes multilabel classification and performance analysis of synchronisation error compensation at varying SNR using Monte Carlo simulations. The interferometric reconstruction and classification accuracy at low SNR conditions is enhanced using multiple receiver or multi channel fusion and the performance of the fusion algorithm is evaluated at varying noise correlation between the fused channels or receivers.

关键词： image fusion inverse synthetic aperture radar (ISAR) multistatic radar object recognition radar imaging radar signal processing radar target recognition remote sensing by radar

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AlignMixup-based ship classification in SAR imagery

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SIGNAL image AND VIDEO processing 2025年第3期19卷 1-10页

作者： Chen, Yongjin An, Wentao Zou, Bin Ren, Peng China Univ Petr East China Coll Oceanog & Space Informat Qingdao 266580 Peoples R China Natl Satellite Ocean Applicat Serv Key Lab Space Ocean Remote Sensing & Applicat Beijing 100081 Peoples R China

This study improves ship classification in Synthetic Aperture Radar (SAR) imagery, focusing on few-shot datasets. We propose a data augmentation strategy combining the AlignMixup method and a detail enhancement module to optimize convolutional neural network performance. AlignMixup integrates features at intermediate layers, capturing structural information, while the detail enhancement module highlights high-frequency details to improve ship feature recognition in SAR images. Experiments on small sample datasets show that our method increases classification accuracy by a significant margin and remains practical under data-limited conditions.

关键词： Data enhancement AlignMixup Ship classification SAR

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Innovative Research on Small Object Detection and recognition in remote sensing images Using YOLOv5 8

Innovative Research on Small Object Detection and Recognitio...

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8th International Conference on Smart Data and Smart Cities, SDSC 2024

作者： Jiang, Shan Huang, He Yang, Junxing Zhang, Xin Wang, Siqi School of Geomatics and Urban Spatial Informatics Beijing University of Civil Engineering and Architecture Beijing China

With the increase of remote sensing image acquisition methods and the number of remote sensing image data, the traditional manual annotation and recognition methods can no longer meet the needs of the present production life. This study explores the use of deep learning techniques to improve the efficiency and accuracy of target detection in remote sensing satellite images, especially for small targets. Traditional target detection methods often face challenges in recognition accuracy and processing speed due to the specificity and complexity of satellite images, such as large size, variable lighting conditions and complex background. Therefore, this paper adopts the YOLOv5 model and introduces the CBAM (Convolutional Block Attention Module) attention mechanism, which significantly improves the detection of small and dense targets. Experimental validation, using the improved YOLOv5 model on the VisDrone2021 dataset, demonstrates that the model improves the mean average percision (mAP) by 1.9% while maintaining real-time performance. This paper provides new ideas for remote sensing image processing, especially for applications in the fields of urban planning and automatic driving, etc. Despite the progress made in this study, the detection of small targets in remote sensing images, the limited classification accuracy and the detection of dynamic targets still need further research. © Author(s) 2024. CC BY 4.0 License.

关键词： Satellite imagery

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recognition and extraction of high-resolution satellite remote sensing image buildings based on deep learning

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NEURAL COMPUTING & APPLICATIONS 2022年第4期34卷 2691-2706页

作者： Zeng, Yifu Guo, Yi Li, Jiayi Changsha Univ Coll Comp Engn & Appl Math Changsha Peoples R China Changsha Univ Hunan Prov Key Lab Ind Internet Technol & Secur Changsha Peoples R China South China Univ Technol Guangzhou Inst Micro Sci & Art SCUT Sch Design State Key Lab Subtrop Bldg Sci Guangzhou 510006 Guangdong Peoples R China Polytech Univ Turin Dept Control & Comp Engn I-10129 Turin Italy

Extracting and recognizing buildings from high-resolution remote sensing images faces many problems due to the complexity of the buildings on the surface. The purpose is to improve the recognition and extraction capabilities of remote sensing satellite images. The Gao Fen-2 (GF-2) high-resolution remote sensing satellite is taken as the research object. The deep convolutional neural network (CNN) serves as the core of image feature extraction, and PCA (principal component analysis) is adopted to reduce the dimensionality of the data. A correction neural network model, that is, boundary regulated network (BR-Net) is proposed. The features of remote sensing images are extracted through convolution, pooling, and classification. Different data collection models are utilized for comparative analysis to verify the performance of the proposed model. Results demonstrate that when using CNN to recognize remote sensing images, the recognition accuracy is much higher than that of traditional image recognition models, which can reach 95.3%. Compared with the newly researched models, the performance is improved by 15%, and the recognition speed is increased by 20%. When extracting buildings with higher accuracy, the proposed model can also ensure clear boundaries, thereby obtaining a complete building image. Therefore, using deep learning technology to identify and extract buildings from high-resolution satellite remote sensing images is of great significance for advancing the deep learning applications in image recognition.

关键词： Deep convolutional neural network High-resolution remote sensing satellite image recognition and processing Building feature extraction Principal component analysis Correction neural network model

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A method for cloud removal in remote sensing images based on attention mechanism and residual symmetric connection structure 12

A method for cloud removal in remote sensing images based on...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Guan, Kang Cheng, Qing Ye, Fan Wang, Haorui China University of Geosciences Wuhan School of Computer Science Wuhan China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Optical remote sensing images have become an important data source for studying the characteristics of the Earth's surface by their wide coverage, high spatial and temporal resolution, and information richness. However, in practical applications, optical remote sensing images are often affected by cloud occlusion, resulting in loss of surface information and errors. To this end, we propose a generative adversarial network (AM-RSC-GAN) based on the attention mechanism and residual symmetric connectivity structure. AM-RSC-GAN uses a multi-scale feature fusion module and the channel spatial attention mechanism to effectively improve the network's extraction and fusion of global and local information of the feature image and adopts the residual symmetric connectivity structure to enabling the network to achieve a good performance between effectively removing the cloud layer and retaining the original detail information. The residual symmetric connection structure enables the network to achieve a good balance between effective cloud removal and preservation of original detail information, better preserving the detail information of the original image and achieving faster convergence speed. We experimentally compare with several previous cloud removal models on the open-source RICE dataset and the WHUS2-CR dataset. The results show that AM-RSC-GAN achieves better results in both peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), proving that the model has excellent cloud removal performance. © 2024 SPIE.

关键词： Optical remote sensing

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Multimodal AI-enhanced ship detection for mapping fishing vessels and informing on suspicious activities

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pattern recognition LETTERS 2025年 191卷 15-22页

作者： Galdelli, Alessandro Narang, Gagan Pietrini, Rocco Zazzarini, Micol Fiorani, Andrea Tassetti, Anna Nora Univ Politecn Marche Dept Informat Engn Ancona Italy Natl Biodivers Future Ctr Palermo Italy CNR Inst Marine Biol Resources & Biotechnol Ancona Italy

Ship detection using remote sensing and data from tracking devices like Automatic Identification System (AIS) playa critical role in maritime surveillance, supporting security, fisheries management, and efforts to combat illegal activities. However, challenges such as varying ship sizes, complex backgrounds, and intentional deactivation of AIS hinder accurate mapping. This study proposes a novel multimodal framework that integrates Sentinel-1 Synthetic Aperture Radar, Sentinel-2 and higher resolution optical imagery. It features an enhanced deep learning-based ship detection model combined with an AIS matchmaking algorithm to detect and cross-reference potentially suspicious maritime activities. The detection model is based on an enhanced You Only Look Once architecture, optimized for identifying small vessels in cluttered and noisy image backgrounds. The model achieves superior performance, surpassing state-of-the-art accuracy on multiple public datasets while reducing training time by 12% compared to baseline models. To ensure transparency within the pipeline, Eigen-CAM explainability techniques were employed, while CO2 emissions were minimized during training using CodeCarbon, aligning the process with environmentally sustainable practices. Finally, the effectiveness of the pipeline was validated in a case study, successfully identifying potential 'dark vessels' and highlighting their possible involvement in suspicious activities.

关键词： Artificial Intelligence Multimodal learning remote sensing Object detection YOLO Vessel tracking AIS

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Research on Regional Water Resources Classification Algorithm Based on Improved Deep Residual Network 6

Research on Regional Water Resources Classification Algorith...

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6th IEEE International Conference on pattern recognition and Artificial Intelligence, PRAI 2023

作者： Xu, Yan Tang, Binhui Chengdu Jincheng College Chengdu China

ISBN: (纸本)9798350325485

In order to realize the rapid and accurate classification of regional water resources, this paper designs the regional water resources classification algorithm based on the improved deep residual network. This paper analyzes the overall situation of regional water resources distribution in China, and takes the remote sensing image of Gaf-2 satellite as the basis of algorithm design, and preprocesses the remote sensing image of regional water resources, such as enhancement, marking and segmentation. On this basis, the pyramid network is used to improve the depth residual network, and the improved depth residual network is combined with the pre-processing results of remote sensing image to obtain the regional water resources classification results. The experimental results show that the proposed algorithm has short preprocessing time, low classification error rate, short classification time and better practical application effect. © 2023 IEEE.

关键词： remote sensing

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A Novel Sinusoid Correction Method of Direct Sun Contamination for Interferometric Microwave Radiometer

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2024年 62卷

作者： Yin, Xiaobin Du, Dunchao Li, Yan Zhou, Wu Ma, Chaofei Zhang, Huan Li, Yinan Ren, Jingjing Ocean Univ China Sanya Oceanog Inst Coll Marine Technol Fac Informat Sci & Engn Qingdao 266100 Peoples R China Qingdao Marine Sci & Technol Ctr SANYA Oceanog Lab Lab Reg Oceanog & Numer Modeling Qingdao 266005 Peoples R China Natl Satellite Ocean Applicat Serv Beijing 100082 Peoples R China Minist Nat Resources Key Lab Space Ocean Remote Sensing & Applicat Beijing 100094 Peoples R China China Acad Space Technol Inst Remote Sensing Satellites Beijing 100094 Peoples R China China Acad Space Technol Xian Xian 710100 Peoples R China

Correction for the impact of direct Sun contamination is a crucial task in the data processing of interferometric microwave radiometer (IMR). The evident presence of solar radiation is observed in the brightness temperature images derived from the Microwave Imaging Radiometer with Aperture Synthesis (MIRAS) payload onboard the Soil Moisture and Ocean Salinity (SMOS) satellite, significantly impacting the data quality to retrieve sea surface salinity (SSS). This article introduces a novel sinusoid correction method for correcting the direct Sun contamination. By leveraging the characteristics of the solar disk, the proposed method simulates and compensates for the contribution of direct solar impact on the spatial frequency domain based on the response pattern of small point sources within the solar disk. The proposed method exhibits a reduced dependency on the precise solar position information and demonstrates resistance to radio frequency interference (RFI), and validations through a simulated IMR and data from in-orbit SMOS confirm the reduction of the direct solar impact on brightness temperature images. The proposed sinusoid correction method outperforms the single and multiple source methods, used in the SMOS operational data processing, especially for the central regions around the location of direct Sun.

关键词： Brightness temperature image direct Sun contamination interferometric microwave radiometer (IMR) sinusoid correction method Brightness temperature image direct Sun contamination interferometric microwave radiometer (IMR) sinusoid correction method

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Analysis of spectral characteristics of typical construction waste

Analysis of spectral characteristics of typical construction...

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2024 International Conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Zhang, Jiayuan College of Geomatics and Geoinformation Guilin University of Technology Guilin China

ISBN: (纸本)9781510680425

With the acceleration of urban construction and renovation, the volume of construction waste has significantly increased, accounting for 30% to 40% of the total urban solid waste emissions. Therefore, analyzing the spectral characteristics of typical construction waste is crucial for remote sensing identification and monitoring, providing essential ground verification information. This study conducted spectral data collection of construction waste in areas such as Daxing District of Beijing and Pingdingshan City, Henan. Various spectral analysis methods were applied to examine its characteristics. The findings revealed that: (1) the wavelength range is between 1913nm and 1932nm, the spectral curves of all kinds of construction wastes have obvious reflection troughs, In the range of 1660-1690nm, organic building materials exhibit absorption valleys. (2) In the wavelength intervals of 600~1100nm and 1860~1930nm, spectral curves exhibit consistent trends, rendering them unsuitable for discrimination. (3) In the range of 1900~2200nm, samples exhibit reduced susceptibility to the spectral characteristics of the dust-proof net, closely mirroring the spectral features of uncovered samples, thereby rendering it suitable as a basis for classification. © 2024 SPIE.

关键词： Reflectivity Absorption Reflection remote sensing Visible radiation Data acquisition Smoothing Spectrometers image classification Solids

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