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IEEE International Geoscience and remote sensing Symposium (IGARSS)

作者： Fu, Jia Shi, Mengyang Xu, Guozheng Huang, Zicheng Zhou, Yue Yang, Fan Gao, Yesheng Liu, Xingzhao Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Peoples R China

ISBN: (纸本)9798350360332;9798350360325

GPR clutter removal significantly benefits subsequent target recognition, detection, and imaging, enhancing the subsequent processing quality. Traditional clutter removal approaches can only remove noise in simple environments. To solve this problem, We proposed a novel improved triplet attention u-net(ITU-Net) which focuses on the hyperbolic feature we need while disregarding irrelevant ground clutter and other background noise. The ITU-Net network enhances image reconstruction capability and facilitates rapid image processing. The improved triplet attention module captures cross-domain interaction between any two domains between H, W, and C and considers long-distance dependencies separately in H, W, and C. The experimental results demonstrate that we can effectively retain the information of the hyperbolas while eliminating noise in complex environments.

关键词： Ground-penetrating radar(GPR) triplet attention module rebar clutter elimination gprMax complex scenes

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Analysis and Modeling Simulation of Hyperspectral Target Characteristics in remote sensing Earth Observation 12

Analysis and Modeling Simulation of Hyperspectral Target Cha...

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

作者： Cong, Longjian Li, Haipeng Wang, Hongshuo Xu, Siyan Huang, Yuhang Beijing Aerospace Automatic Control Institute Beijing100854 China Huazhong University of Science and Technology Hubei Province Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Aiming at the demand for target detection of typical ground and object backgrounds in hyperspectral earth observation, this project solves the problems of insufficient sample size and low fidelity of target environment scene in intelligent learning, training and testing of a novel optoelectronic detection system, breaks through the key technologies of modeling of hyperspectral imaging simulation mechanism, modeling of hyperspectral reflectance/radiation characteristics of typical target background, computation of hyperspectral atmospheric transmission characteristics, and modeling of hyperspectral optical detectors, etc., and organically integrates remote sensing imaging and spectral measurement technologies. Through simulation, it simultaneously acquires spatial and spectral radiation information data from typical target backgrounds, thus providing foundational data support for the design of target detection algorithms based on hyperspectral data. © 2024 SPIE.

关键词： Hyperspectral imaging

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SSTNet: spatial-spectral transformer network for panchromatic sharpening

SSTNet: spatial-spectral transformer network for panchromati...

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

作者： Wang, Shuxiang Jin, Fei Rui, Jie Lin, Yuzhun Niu, Yueqin Information Engineering University China Shan Dong Agriculture and Engineering University China

ISBN: (纸本)9781510680425

In the field of remote sensing, panchromatic sharpening technology integrates spatial data from panchromatic images with spectral data from multispectral images to generate high-resolution multispectral *** mapping from multispectral to single-band panchromatic images greatly impacts the quality of fusion *** paper introduces the spatial-spectral transformer network (SSTNet).SSTNet combines the spatial constraints of the difference model in the gradient domain and the intensity constraints in the spectral domain to quantitatively express multispectral imagery into panchromatic imagery in a many-on-one form, laying the groundwork for designing the loss function in image *** to the original method, SSTNet is applied to *** on the Quickbird dataset demonstrate that the evaluation indexes of the SSTNet-based P2Sharpen method are improved in both reduced and full resolution resolution, requiring fewer training data and epoch. © 2024 SPIE.

关键词： image fusion

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Handwritten image recognition based on CNN Algorithm

Handwritten Image Recognition based on CNN Algorithm

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2024 International Conference on remote sensing, Mapping, and image processing, RSMIP 2024

作者： Huang, Zhihao Huaqiao University Fujian Quanzhou362021 China

ISBN: (纸本)9781510680012

The optimization of image recognition technology provides a platform and possibility for the dissemination and protection of most of the information documents, especially font and number recognition, which is an important part of it. At present, the recognition accuracy of standard fonts and numerals has reached its maximum with continuous optimization, but the recognition of handwritten numerals still needs to be further improved. The aim of this paper is to test the correct recognition rate of handwritten numerals using a convolutional neural network model. Through the elaboration of the basic principles and structure of the neural convolutional network algorithm and the purpose of the article, the relevant test model was established. And the MINST dataset is used to train the model and test the recognition effect of handwritten numbers. Through experiments, the model used in this paper can correctly recognize most handwritten digits, thus satisfying the purpose of the experimental design, and providing new support for experiments on handwritten fonts. © 2024 SPIE.

关键词： Neural network models

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UAM-Net: An Attention-Based Multi-level Feature Fusion UNet for remote sensing image Segmentation 6th

UAM-Net: An Attention-Based Multi-level Feature Fusion UNet ...

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6th Chinese Conference on pattern recognition and Computer Vision (PRCV)

作者： Cao, Yiwen Jiang, Nanfeng Wang, Da-Han Wu, Yun Zhu, Shunzhi Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Fujian Key Lab Pattern Recognit & Image Understan Xiamen 361024 Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Semantic segmentation of remote sensing images (RSIs) is an essential application for precision agriculture, environmental protection, and economic assessment. While UNet-based networks have made significant progress, they still face challenges in capturing long-range dependencies and preserving fine-grained details. To address these limitations and improve segmentation accuracy, we propose an effective method, namely UAM-Net (UNet with Attention-based Multi-level feature fusion), to enhance global contextual understanding and maintain fine-grained information. To be specific, UAM-Net incorporates three key modules. Firstly, the Global Context Guidance Module (GCGM) integrates semantic information from the Pyramid Pooling Module (PPM) into each decoder stage. Secondly, the Triple Attention Module (TAM) effectively addresses feature discrepancies between the encoder and decoder. Finally, the computation-effective Linear Attention Module (LAM) seamlessly fuses coarse-level feature maps with multiple decoder stages. With the corporations of these modules, UAM-Net significantly outperforms the most state-of-the-art methods on two popular benchmarks.

关键词： Semantic segmentation U-shape architecture Attention mechanism Feature fusion remote sensing images

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Motion ship tracking method based on ship wake recognition in GF-4 remote sensing images 6

Motion ship tracking method based on ship wake recognition i...

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6th International Conference on Geoscience and remote sensing Mapping, GRSM 2024

作者： Qi, Wenping Wang, Wenyu Huang, Shusong Shi, Xiaojin Li, Liutong China Centre for Resources Satellite Data and Application No. 5 Fengxian East Road Beijing100094 China

ISBN: (纸本)9781510688018

To achieve continuous monitoring and tracking of ships over a long time series, a method based on multi-temporal remote sensing images from GF-4 satellite for continuous monitoring and tracking of ship wakes is provided. This method first constructs a GF-4 satellite remote sensing dataset for ship wake recognition, performs texture enhancement processing on the remote sensing images, and finally trains the model based on the YOLOv5x network. Experimental results show that image texture enhancement can significantly improve the recognition effect, and the highest detection rate can reach 91% in cloudless and fragmented cloud environments. Through testing, the single-band imaging mode can be used for continuous tracking and monitoring of multiple targets in the same area, obtaining motion information such as target position, heading, speed, and trajectory. © 2025 SPIE.

关键词： image texture

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David A. Landgrebe: Evolution of Digital remote sensing and Landsat

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2022年 15卷 4835-4860页

作者： Goward, Samuel N. Bauer, Marvin E. Biehl, Larry L. Hall, Forrest G. Hoffer, Roger M. Richards, John A. Rocchio, Laura E. P. Salomonson, Vincent V. Williams, Darrel L. Univ Maryland Dept Geog Sci College Pk MD 20742 USA Univ Minnesota Twin Cities St Paul MN 55108 USA Purdue Univ W Lafayette IN 47906 USA Hopkins Odyssey Program Baltimore MD 21218 USA Colorado State Univ Dept Forest Sci Ft Collins CO 80523 USA Australian Natl Univ Canberra ACT 2601 Australia Sci Syst & Applicat Inc Lanham MD 20706 USA NASA Goddard Space Flight Ctr Greenbelt MD 20771 USA Global Sci & Technol Inc Greenbelt MD 20770 USA

David Landgrebe, Professor of Electrical Engineering and Director of the Purdue University Laboratory for Applications of remote sensing (LARS), was a primary innovator in the field of digital image analysis and remote sensing of the environment. He and his LARS colleagues, along with a selected few other researchers at institutions including the University of Michigan, University of California Berkley, NASA Goddard Space Flight Center, and NASA Johnson Space Center, defined and developed remote sensing technology to monitor the Earth's terrestrial environment. This research led to the Landsat program, which has continued to monitor the Earth's land areas for a half century. These technologies have defined new fields of scientific query in digital image analysis, biophysical remote sensing, as well as remote sensing science and applications. Dr. Landgrebe's contributions to these research areas were substantial and profound. Understanding the early evolution of work is critical to understanding how this technology is still advancing today. The authors hope that current and future students of these fields will benefit from understanding how this all began.

关键词： remote sensing Earth NASA Artificial satellites Electronic mail Crops Forestry Agricultural remote sensing clustering digital image display digital image processing digital remote sensing Earth system science feature selection field studies multispectral sensor multitemporal data pattern recognition radiative transfer (RT) model remote sensing science spectral vegetation indices (SVI)

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MTP-Net: Road extraction from remote sensing images based on multi-type pooling network

MTP-Net: Road extraction from remote sensing images based on...

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

作者： Wei, Zhiheng Zhang, Zhenyu College of Information Science and Engineering Xinjiang University Urumqi830017 China Key Laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region Xinjiang University Urumqi830017 China

ISBN: (纸本)9781510666351

As a popular task in remote sensing, road extraction has been widely concerned and applied by researchers, especially by using deep learning methods. However, many methods ignore the properties of roads in remote sensing images, which have long-range structures or discrete distributions. Therefore, this paper designs a network(MTP-Net) based on strip pooling and multi-scale spatial pooling. This network uses the ResNet50 network as the encoder to achieve feature extraction and ensures the overall connectivity and edge details of roads by the multi-type pooling module including strip pooling and multi-scale spatial pooling. The MTP-Net was tested on the Massachusetts Roads Dataset, the F1-score and IoU(intersection ratio) reached 72.24% and 56.54%, respectively. Compared with the mainstream methods such as UNet and deeplabV3+, the experiment shows that the MTP-Net is superior to the comparison model and has good results in road extraction. © 2023 SPIE.

关键词： remote sensing

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image denoising application in visual creation of art design based on intelligent algorithm

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SOFT COMPUTING 2023年 1-11页

作者： Xinong, Huang Quanzhou Normal Univ Coll Art & Design Quanzhou 362000 Fujian Peoples R China

In today's era of information, digitization and intelligence, the share of graphic information is increasing, and it has become an important tool for receiving and transmitting information. Digital images are widely used in various fields such as computer vision, medical imaging, and remote sensing. However, the process of acquisition and transmission can often cause distortion and noise, which can significantly reduce the quality of the image. Therefore, image denoising is an important post-processing step that aims to suppress noise and improve the image quality. Speech recognition has become one of the most important technologies for improving intelligent human-computer interaction. With the increasing popularity of voice interactive products and services, speech recognition technology has been widely researched and applied in various fields. Meanwhile, the importance of design education has been recognized and given more attention in the context of basic education reform. The creation of a "design and application" research field can promote the development of design education and help students develop their creativity, spatial perception ability, and aesthetic ability.

关键词： Speech recognition image denoising Gaussian mixture model Creative education

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Research on Deep Learning-Based Land Cover Extraction Method using Multi-Source Mixed Samples 12

Research on Deep Learning-Based Land Cover Extraction Method...

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

作者： Lan, Yizhou Hu, Qingwu Wang, Shunli Li, Jiayuan Zhao, Pengcheng Ai, Mingyao School of Remote Sensing and Information Engineering Wuhan University 129 Luoyu Road Hongshan District Hubei Province Wuhan China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

The advancement of Earth observation technologies has enabled comprehensive Earth monitoring at unprecedented levels. Single remote sensing images often fall short in practical applications due to various limitations. In response, the field of remote sensing has evolved to encompass multiple temporal phases, wavelengths and sensors, resulting in a wealth of multi-source remote sensing data. However, most studies have primarily employed single remote sensing images as their data source, with limited research on deep learning for land cover classification using multi-source mixed samples. There is limited research on the influence of network band selection strategies for different land cover categories. Research into network band selection strategies further enhances the accuracy of land cover extraction from multi-source data. Consequently, this paper presents a methodology for deep learning with multi-source mixed samples, investigating strategies for selecting optimal spectral bands for different land cover categories and ultimately aiming to extract various typical land cover classes from multi-source remote sensing data within the same geographic region. We begin by creating a multi-source remote sensing mixed sample dataset and employ channel enhancement methods to enable the model to predict data with different spectral band combinations. Subsequently, we investigate the network's selection of optimal spectral bands for different land cover categories to enhance land cover extraction accuracy. The results indicate that our approach not only improves accuracy but also enhances the model's generalization capabilities. © 2024 SPIE.

关键词： remote sensing

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