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25th IEEE China conference on System Simulation Technology and its Application, CCSSTA 2024

作者： Gao, Gemengyue Xin, Yu Li, Yingying Chen, Feng Feng, Xin Wang, Xueying Lin, Yuzhuoyue Zhou, Hongyuan Beijing Institute of Remote Sensing Information Beijing China

ISBN: (纸本)9798350366600

Synthetic Aperture Radar (SAR), with its all-weather and all-day operational capabilities and wide-area detection ability, has become an important means for global monitoring of terrain and oceans. The fusion processing of SAR images with other sensor data can fully utilize the complementary advantages of information from various sensors to obtain richer and more targeted fusion results. This paper first analyzes and summarizes the SAR and opticalimage fusion processing techniques, including the main methods of pixel-level, feature-level, and decision-level fusion;then introduces the SAR and Automatic Identification System (AIS) data fusion processing technology, including data association methods based on location feature information and basedon multi-feature fusion, as well as ship fusion detection and recognition methods;finally, it summarizes the aforementioned methods. © 2024 IEEE.

关键词： image fusion

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Railway facilities safety monitoring system based on space-air-ground multi-source data

Railway facilities safety monitoring system based on space-a...

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

作者： Xue, Aliang Guo, Hang Zhang, Na Miao, Long China Coal Aerial Remote Sensing Group Co. Ltd. 216 Shenzhou Fourth Road Xi'an China Xi'an Rail Transit Group Co. Ltd. 126 Fengcheng 8th Road Xi'an China

ISBN: (纸本)9781510666351

In view of many problems existing in the current railway transportation process, such as pedestrian crossing the guardrail, landslide, rock rolling, debris flow and other factors, this paper summarizes the research status of the underground railway foreign body intrusion monitoring system at home and abroad, and puts forward a railway facility safety monitoring system based on multi-source data from the sky and the sky. The system uses satellite, unmanned aerial vehicle, distributed optical fiber sensor, video, GNSS and GIS technologies to realize the safety monitoring of railway facilities, and formulates the technical scheme for the safety information management of space-based railway facilities, and tests and evaluates the research results through the test section. The development trend of railway facilities safety monitoring system is prospected as follows: foreign body intrusion monitoring at the boundary of key sections;Real-time monitoring of tunnel entrance and high slope section stones;Carry out dynamic monitoring of off-road safety environment. © 2023 SPIE.

关键词： Optical fibers

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Satellite image Classification Using ANN 1st

Satellite Image Classification Using ANN

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1st International conference on Cyber Warfare, Security and Space Research, SpacSec 2021

作者： Mathur, Pratistha Kavita Manipal University Jaipur Jaipur India

ISBN: (纸本)9783031157837

Every human being is capable of recognizing patterns. There are various problems in domains like bioinformatics, data mining, document classification, image analysis, remote sensing, and speech recognition where automatic recognition is needed. The rapid growth in computing power enables the development of such applications. remote sensing is one of the areas where a huge amount of data is available. In this paper, we have explored the supervised and unsupervised ANN for classifying satellite images. The adaptive pattern recognition capability of ANN is used to simulate the spatial variations in the images of an object/area. Multi-layer feed-forward neural network using backpropagation learning method and self-organizing feature map (SOFM) has experimented. Experiments are implemented using MATLAB. It is hoped that this multidisciplinary work will be useful for researchers and image processing users in the field of computer science and remote sensing. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： MATLAB

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Deep Learning-Based Scene Classification for remote sensing images 3rd

Deep Learning-Based Scene Classification for Remote Sensing ...

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3rd World conference on Intelligent and 3D Technologies, WCI3DT 2024

作者： Liang, Zikang Jin, Runpeng Tian, Hongwei Electronics and Information Engineering Nanjing University of Aeronautics and Astronautics Jiangsu Nanjing China

ISBN: (纸本)9789819791231

remote sensing image (RS) classification has a broad range of potential applications in various fields. However, remotely sensed images comprise various land cover elements arranged in intricate spatial patterns, presenting a challenge for accurate recognition. Fortunately, breakthroughs in deep learning techniques brought new opportunities for revolutionizing RS image processing. The convolutional neural network (CNN), as a deep learning technique, has garnered considerable attention in recent years, particularly for its widespread application in remote-sensing image scene classification. Recent advancements have demonstrated its effectiveness in this domain. This study uses CNN as the basis for the land-use scene classification task. We chose the UC-Merced dataset as the experimental object, which contains 21 different categories of land-use scene images. Then, we leverage a modified version of the ResNet50 model to overcome the feature degradation problem in the deep learning process. The experimental results show that ResNet50 with transfer learning exhibits the best performance in the land-use scene classification task, and its accuracy (95.29% (±0.66%)), precision (95.35% (±0.68%)), F1-score (95.23% (±0.67%)), and Kappa coefficient (95.05% (±0.70%)) are all better than the other models. In conclusion, this study serves as a valuable reference for selecting deep learning models for land-use scene classification tasks and offers insightful guidance for future research and applications in related domains. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Controlled Multi-modal image Generation for Plant Growth Modeling 26

Controlled Multi-modal Image Generation for Plant Growth Mod...

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26th International conference on pattern recognition / 8th International Workshop on image Mining - Theory and Applications (IMTA)

作者： Miranda, Miro Drees, Lukas Roscher, Ribana Univ Bonn IGG Remote Sensing Niebuhrstr 1a D-53113 Bonn Germany Tech Univ Munich Data Sci Earth Observat Lise Meitner Str 9 D-85521 Ottobrunn Germany German Res Ctr Artificial Intelligence DFKI Trippstadter Str 122 D-67663 Kaiserslautern Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Predicting plant development is an important task in precision farming and an essential metric for decision-making by researchers and farmers. In this work, we propose a novel generative modeling technique for plant growth prediction based on conditional generative adversarial networks. We formulate plant growth as an image-to-image translation task and predict the appearance of a plant growth stage as a function of its previous stage. We take into account that plant growth is inherently multi-modal, depending on numerous and highly variable environmental factors, and thus a single input belongs to a distribution of potential outputs. We encode the ambiguity in an interpretable and low-dimensional latent vector space representing the various factors of variation that are influencing plant growth. We use a novel encoder-based data fusion technique and combine information contained in remote sensing imagery of different cropping systems with data containing the factors of variation to adequately model plant growth. This offers several advantages over existing methods: (1) we show that we can model a distribution of potential appearances and simultaneously outperform existing methods in providing more realistic predictions, (2) the complexity of plant growth is more adequately captured, as various factors influencing plant growth can be included, (3) predictions are controllable by being conditioned by an interpretable latent vector representing the factors of variation along with an input image of a previous growth stage.

关键词： Measurement image synthesis Ecosystems Predictive models Generative adversarial networks pattern recognition Task analysis

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DS-Swin Transformer HRNet for remote sensing images 4

DS-Swin Transformer HRNet for Remote sensing images

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4th International conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Wu, Guinan Wu, Qinghong University of Science and Technology Liaoning Liaoning Province Anshan China

ISBN: (纸本)9781510682528

With the continuous development of remote sensing imagery in deep learning, this paper proposes a self-attention model called Dual-Stream Swin Transformer to address the computational and memory requirements issues traditional Transformers face when dealing with high-resolution images. Specifically, 1) The Dual-Stream Swin Transformer in this paper adopts an innovative approach by decomposing the traditional Transformer encoder layer into smaller building blocks and introducing a shifted windows mechanism to construct self-attention. 2) Traditional Transformer models require significant computational and storage resources when processing high-resolution images because they perform self-attention calculations on the entire global image. However, the Swin Transformer significantly reduces the computational and memory requirements by segmenting the image into multiple spatially overlapping windows and performing self-attention calculations within each window. 3) Furthermore, the introduced shifted windows mechanism ensures that the attention of each window is only related to its adjacent windows, further reducing the computational complexity. This decomposition and window mechanism combination makes the Swin Transformer ideal for handling high-resolution visual inputs. It achieves high precision while offering higher computational efficiency and lower memory consumption. This makes the Swin Transformer perform excellently in image classification, object detection, and semantic segmentation tasks. We conducted comparative experiments between this model and other classical network models of the same type. The Dual-Stream Swin Transformer in this paper effectively addresses traditional Trans-formers' computational and memory challenges when handling high-resolution images through innovative decomposition and window mechanisms, providing a new solution for efficiently processing large-scale visual data. © 2024 SPIE.

关键词： Semantic Segmentation

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remote sensing images Semantic Segmentation Method Based on Improved Nested UNet

Remote Sensing Images Semantic Segmentation Method Based on ...

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2022 International conference on Geographic Information and remote sensing Technology, GIRST 2022

作者： Li, Zhongyu Liu, Yang Kuang, Yin Wang, Huajun Liu, Cheng College of Geophysics Chengdu University of Technology Chengdu610059 China College of Computer Science Chengdu Normal University Chengdu611130 China Key Laboratory of interior Layout optimization and Security Institutions of Higher Education of Sichuan Province Sichuan Chengdu611130 China Key Laboratory of Pattern Recognition and Intelligent Information Processing of Sichuan Chengdu University Chengdu610106 China Artificial Intelligence Key Laboratory of Sichuan Province Zigong643000 China

ISBN: (纸本)9781510662186

With the development of remote sensing technology, remote sensing images of buildings are of great significance in urban planning, disaster response, and other directions. When we use a neural network containing batch normalization layers for semantic segmentation, the neural network is sensitive to batch size and has low segmentation accuracy for occluded and dense buildings. This paper proposes a method for building segmentation in remote sensing images based on Nested UNet (UNet++) deep neural network. First, the UNet++ network is used to extract features, and the Group Normalization (GN) method is used instead of Batch Normalization (BN) to alleviate the model's sensitivity to batch size. Then the weighted combination of Cross-Entropy Loss (CELoss) and DiceLoss is used as the loss function to improve the feature extraction ability of the neural network for unbalanced buildings. Finally, experiments are carried out on the WHUBuilding dataset. The experimental results show that the improved model (UNet++-GN) improves Mean Intersection over Union (MIoU) and Mean Pixel Accuracy (Macc) by 12.16% and 2.92%, respectively, compared with the original model (UNet++-BN). © 2023 SPIE.

关键词： Feature extraction

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Realization Technology of Texture Feature Extraction Algorithm of remote sensing Satellite image Based on FPGA 2

Realization Technology of Texture Feature Extraction Algorit...

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2nd IEEE International conference on Data Science and Computer Application, ICDSCA 2022

作者： Feng, Kai Zhang, Rui Shaanxi Academy of Aerospace Technology Application Co. Ltd. Xi'an China

ISBN: (数字)9781665472005

ISBN: (纸本)9781665472005

In recent years, the research on remote sensing image information output has developed rapidly, including not only remote sensing technology, image processing, pattern recognition, etc., but also land use, environmental monitoring, disaster forecasting, urban planning and other fields. The purpose of this paper is to study the realization technology of remote sensing satellite image texture feature extraction algorithm based on FPGA. A hardware system implementation scheme based on FPGA is proposed. Firstly, the overall structure of the system is established, and the software and hardware platforms are selected. Then, according to the difficulty of the algorithm, it is proposed to divide the algorithm into two parts, and the system based on hardware logic and based on soft core processor is described in detail. Implementation plan. An object modeling method based on Gabor texture blocks is introduced. This method is applied to the extraction of urban buildings from remote sensing images, and the final building extraction accuracy reaches 91.5%. © 2022 IEEE.

关键词： Field programmable gate arrays (FPGA)

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An Automated Analysis Tool for the Classification of Sea Surface Temperature imagery

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pattern recognition AND image ANALYSIS 2022年第3期32卷 631-635页

作者： Reggiannini, M. Papini, O. Pieri, G. Natl Res Council Italy Inst Informat Sci & Technol I-56124 Pisa Italy

Sea observation through remote sensing technologies plays an essential role in understanding the health status of the marine coastal environment, its fauna species and their future behavior. Accurate knowledge of the marine habitat and the factors affecting faunal variations allows us to perform predictions and adopt proper decisions. This paper concerns the proposal of a classification system devoted to recognizing marine mesoscale events. These phenomena are studied and monitored by analyzing sea surface temperature imagery. Currently, the standard way to perform such analysis relies on experts manually visualizing, analyzing, and tagging large imagery datasets. Nowadays, the availability of remote sensing data has increased so much that it is desirable to replace the labor-intensive, time-consuming, and subjective manual interpretation with automated analysis tools. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula.

关键词： image processing remote sensing mesoscale patterns sea surface temperature machine learning climate change

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Supervised Spiking Neural Network for SAR image recognition: Analysis of Encoding Methods and Performance under Strong Noise Influence 12

Supervised Spiking Neural Network for SAR Image Recognition:...

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

作者： Junyu, Wang Hao, Sun Tao, Tang Lin, Lei Kefeng, Ji National University of Defense Technology 109 Deya Road Kaifu District Hunan Changsha410003 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

With the rapid development of artificial neural network (ANN), the field of synthetic aperture radar (SAR) target recognition has witnessed significant progress. However, due to the poor interpretability and ease of being affected by speckle noise, it brings challenges to ANN for SAR target recognition. Spiking Neural Network (SNN) has emerged as the third-generation neural network architecture and presents promising prospects for various applications. This study aims to explore the performance of SNN in SAR target recognition. In our experiments, we achieved comparable performance to conventional neural networks by utilizing directly trained SNN. This indicates the effectiveness of SNN in coping with SAR target recognition tasks. Moreover, we investigated the impact of different spiking encoders on SAR target recognition. Specifically, we compared the performance of SNN using the Poisson encoder and utilizing the first layer of the SNN as an encoder. This comparison provides valuable insights into the optimal coding strategy for SNN-based SAR target recognition. Additionally, we examined the robustness of SNNs in the presence of strong speckle noise. Our findings demonstrate that SNN can maintain good performance under the influence of strong speckle noise. The outcomes of this research shed light on the potential of SNN as a powerful tool for SAR target recognition. Future studies can focus on exploring SNN's applicability to SAR Interpretation. © 2024 SPIE.

关键词： Synthetic aperture radar

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