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检索条件"任意字段=Conference on Image Processing and Pattern Recognition in Remote Sensing"

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EarthLoc: Astronaut Photography Localization by Indexing Earth from Space

EarthLoc: Astronaut Photography Localization by Indexing Ear...

引用

IEEE/CVF conference on Computer Vision and pattern recognition (CVPR)

作者： Berton, Gabriele Stoken, Alex Caputo, Barbara Masone, Carlo Politecn Torino Turin Italy NASA Jacobs Technol Johnson Space Ctr Houston TX USA

ISBN: (纸本)9798350353006

Astronaut photography, spanning six decades of human spaceflight, presents a unique Earth observations dataset with immense value for both scientific research and disaster response. Despite their significance, accurately localizing the geographical extent of these images, which is crucial for effective utilization, poses substantial challenges. Current, manual localization efforts are time-consuming, motivating the need for automated solutions. We propose a novel approach - leveraging image retrieval - to address this challenge efficiently. We introduce innovative training techniques which contribute to the development of a high-performance model, EarthLoc. We develop six evaluation datasets and perform a comprehensive benchmark comparing EarthLoc to existing methods, showcasing its superior efficiency and accuracy. Our approach marks a significant advancement in automating the localization of astronaut photography, which will help bridge a critical gap in Earth observations data. Code and datasets are available at https://***/gmberton/EarthLoc.

关键词： dataset image retrieval large scale remote sensing visual geolocalization Visual Localization

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Stacking algorithm based on naive Bayes 2

Stacking algorithm based on naive Bayes

引用

2nd Asia conference on Computer Vision, image processing and pattern recognition (CVIPPR)

作者： Huang, Chen Zhou, Yuting Yang, Xuemei Liu, Shiqi Yin, Junping Northeast Normal Univ Acad Adv Interdisciplinary Studies Jilin Jilin Peoples R China Beijing Inst Remote Sensing Beijing Peoples R China China Acad Engn Phys Computat Math Mianyang Sichuan Peoples R China Beijing Inst Appl Phys & Computat Math Big Data Ctr Beijing Peoples R China

ISBN: (纸本)9798400716607

With the advent of the data era, medical image data plays an increasingly important role in diagnosing and treating diseases. This study combined deep learning, ensemble learning, and mathematical theory to construct a Stacking algorithm model based on the Naive Bayes method to achieve more accurate classification of medical images. In the Stacking algorithm, the results of the primary classifier are treated equally, and the performance of the primary classifier is ignored. The poor performance of the primary classifier will affect the final classification result. To solve this problem, a Stacking algorithm based on the Naive Bayes method is proposed in this paper. Firstly, the performance of the primary classifier is evaluated, and the output of the primary classifier is selected reasonably through the evaluation results. Secondly, the VGGNet, InceptionNet, and ResNet convolutional networks were used as primary classifiers to construct a complete Stacking algorithm based on the Naive Bayes method. Next, the algorithm was tested and validated using COVID-19 and NoCOVID-19 lung CT image data. Finally, it is compared with a single primary classifier and the traditional Stacking algorithm. The experimental results show that the Stacking algorithm based on the Naive Bayes method is suitable for the binary classification task of medical images and has better classification results than a single primary classifier and the traditional Stacking algorithm.

关键词： Stacking algorithm Naive Bayes method Convolutional neural network Medical image

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Design of Cloud Platform for Efficient remote sensing Big Data processing Based on Open Stack

Design of Cloud Platform for Efficient Remote Sensing Big Da...

引用

2023 International conference on Integrated Intelligence and Communication Systems, ICIICS 2023

作者： Huang, Yan Zhang, Weimeng Xi'an Traffic Engineering Institute Shaanxi Xi'an China Xi'an Technical School Shaanxi Xi'an China

ISBN: (纸本)9798350315455

The rapid preparation of computing resources is an important step to realize efficient remote sensing image processing. It takes a lot of time for researchers to prepare hardware resources and deploy computing framework. It often mistakes in the preparation and careless deployment of the framework which can lead to high research costs. To solve this problem, this paper uses Open Stack to virtualize hardware resources and uses Linux Shell scripting language to quickly prepare and deploy the Spark parallel computing environment. In this paper, based on the remote sensing image change detection method of connection condition, for example, the data processing algorithm in advance import platform algorithms library, component library, used to support the remote sensing image change detection of business flow of creation and recognition, resources rapid preparation, execution and presentation of the results of the algorithm and implement of massive remote sensing image change detection and efficient. It effectively improves the efficiency of remote sensing data management. © 2023 IEEE.

关键词： linux shell open stack remote sensing data management spark

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DBDAN: Dual-Branch Dynamic Attention Network for Semantic Segmentation of remote sensing images 6th

DBDAN: Dual-Branch Dynamic Attention Network for Semantic Se...

引用

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

作者： Che, Rui Ma, Xiaowen Hong, Tingfeng Wang, Xinyu Feng, Tian Zhang, Wei Zhejiang Univ Sch Software Technol Hangzhou 310027 Peoples R China Zhejiang Univ Innovat Ctr Yangtze River Delta Jiaxing 314103 Peoples R China

ISBN: (纸本)9789819984619;9789819984626

Attention mechanism is capable to capture long-range dependence. However, its independent calculation of correlations can hardly consider the complex background of remote sensing images, which causes noisy and ambiguous attention weights. To address this issue, we design a correlation attention module (CAM) to enhance appropriate correlations and suppress erroneous ones by seeking consensus among all correlation vectors, which facilitates feature aggregation. Simultaneously, we introduce the CAM into a local dynamic attention (LDA) branch and a global dynamic attention (GDA) branch to obtain the information on local texture details and global context, respectively. In addition, considering the different demands of complex and diverse geographical objects for both local texture details and global context, we devise a dynamic weighting mechanism to adaptively adjust the contributions of both branches, thereby constructing a more discriminative feature representation. Experimental results on three datasets suggest that the proposed dual-branch dynamic attention network (DBDAN), which integrates the CAM and both branches, can considerably improve the performance for semantic segmentation of remote sensing images and outperform representative state-of-the-art methods.

关键词： remote sensing Semantic Segmentation Attention Mechanism Deep Learning

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An iterative attention context fusion network for semantic segmentation of remote sensing image 12

An iterative attention context fusion network for semantic s...

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

作者： Chen, Yanpu Wang, Haihui Zhang, Shuiping Hu, Po Hubei Key Laboratory of Intelligent Robot Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In recent years, high-resolution remote sensing image segmentation has become a key task involving technology in many fields, mainly used to accurately extract target information from remote sensing images, and widely used in land detection, coverage classification, etc. These images are characterized by high resolution and large scale, and the overall segmentation requires high performance of hardware devices, the downsampling method usually used is easy to affect the segmentation quality, cropping and slicing the image will lead to the lack of edge information, and the problems of category homogenization, changes in complex scenes, and noise and occlusion make the segmentation challenging. Therefore, in this paper, we propose An iterative attention context fusion network (IACFNet) based on the attention mechanism as well as information fusion at different scales, which iteratively calculates the attention module weights connecting low and high features and refines the problem of spatial information loss, utilizing multiscale segmentation for information complementation, and utilizing an improved boundary loss function to precisely define boundary instances. Our proposed method obtains a performance improvement of about 2.8% and 1.5% in the mean intersection over union (mIoU) metric compared to the current state-of-the-art methods, respectively. © 2024 SPIE.

关键词： Semantics

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Study on multi-annual and simultaneous satellite-ground remote sensing retrieval of water transparency (Secchi disk depth) in Poyang lake

Study on multi-annual and simultaneous satellite-ground remo...

引用

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

作者： Zheng, Pu Xu, Xin-Fa Xu, Xiao-Hua Huang, Ping Zhou, Xin-Wen Jiang xi Academy of Water Science and Engineering Nanchang330029 China Jiangxi Provincial Technology Innovation Center for Ecological Water Engineering in Poyang Lake Basin 330029 China

ISBN: (纸本)9781510680425

Poyang Lake is the largest freshwater lake in China. It is located in the middle and lower reaches of the Yangtze River in the north-central Jiangxi Province. As the largest Lake which is connecting the Yangtze River, the water ecological security of Poyang Lake affects the entire Yangtze River basin. Water transparency(SDD) is an important indicator of water quality, so it is of great significance to carry out routine monitoring of SDD using remote sensing methods. This study takes Poyang Lake as the experimental area, and uses the SDD as the inversion index to characterize water quality. Based on observation data from 2015 to 2020 and Landsat series images, the inversion of SDD model suitable for summer Poyang Lake was constructed by analyzing and comparing the correlation coefficients between different wave bands and the SDD under the condition of standardized modeling phase and "star-ground" collaborative matching. After verification by four sets of independent data, the mean R2 is 0.8621, the mean RSME is 15.37cm, and the mean MAPE is 23.6%. It is believed that the method used in this study can be well applied to the inversion of SDD in the Poyang Lake area, and also provides a new reference method for monitoring the transparency of inland water bodies under long-term observation conditions. © 2024 SPIE.

关键词： Lakes

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Patch-based Cross-domain Adaptation for Few-shot remote sensing Scene recognition 2

Patch-based Cross-domain Adaptation for Few-shot Remote Sens...

引用

2nd IEEE International conference on Signal, Information and Data processing, ICSIDP 2024

作者： He, Chengzhi Liu, Yu Jiang, Zhizhuo Li, Yaowen Yan, Chenggang Zheng, Bolun Tsinghua University Shenzhen International Graduate School Shenzhen China Tsinghua University Department of Electronic Engineering Beijing China Hangzhou Dianzi University Intelligent Information Processing Laboratory Hangzhou China

ISBN: (纸本)9798331515669

Recognizing new classes from a few labeled remote sensing images presents a significant challenge. Existing methodologies assume that training and testing datasets come from identical domains. Collecting training datasets of remote sensing images from each domain is practically unfeasible. To tackle this challenge, we propose a strategy that leverages image patch feature embedding and cross-adaptation mechanism for cross-domain few-shot classification. Initially, the supplied image is segmented into patches of the same size. Subsequently, feature embeddings are extracted and reinforced with prototypical triplet loss to enhance robustness. A unified module then facilitates cross-adaptation between the support and query sets, producing classification features that effectively represent the relationship between them, thus better adapting to few-shot remote sensing classification challenges with domain shifts. Our proposed method's excellence and generality are confirmed by comprehensive experiments. © 2024 IEEE.

关键词： Jurassic

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Study on Hyperspectral remote sensing images of GF-5 De-Blurring Based on Sparse Representation 2023

Study on Hyperspectral Remote Sensing Images of GF-5 De-Blur...

引用

5th International Symposium on Signal processing Systems, SSPS 2023

作者： Wang, Hong School of Electronic Information Sichuan Modern Vocational College Chengdu China

ISBN: (纸本)9798400700040

Deblurring high resolution remote sensing image is a very important problem in remote sensing research. In this paper, we propose a new deblurring algorithm for high-resolution remote sensing images (HSI) based on sparse representation. The purpose of this study is to apply compressed sensing measurement and reconstruction technology to realize the processing of remote sensing image, and discuss the Under what circumstances can CS achieve better results in remote sensing image processing. The algorithm uses fast gradient projection algorithm to achieve deblurring and retain the important ground information of the original image. Experiments on remote sensing images obtained by GF-5 show that the algorithm can filter the blurring of remote sensing images well and improve the peak-to-noise ratio (PSNR) of images. The algorithm has better performance than other sparse representation algorithms. This paper explores the application of dictionary learning theory and sparse decomposition in remote sensing image processing. By further extending the algorithm proposed in this paper and adding new constraints, remote sensing image restoration, target recognition, deblurring, fusion and so on can be carried out. © 2023 ACM.

关键词： image reconstruction

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remote sensing image Compression Method Based on Implicit Neural Representation 12

Remote Sensing Image Compression Method Based on Implicit Ne...

引用

12th International conference on Computing and pattern recognition, ICCPR 2023

作者： Li, Xin Sun, Baile Liao, Jixiu Zhao, Xiaofei Shandong Qingdao China

ISBN: (纸本)9798400707988

Oceanography-oriented remote sensing image is characterized by being multi-source, heterogeneous, and massive. In recent years, the scale of remote sensing data is exploding, necessitating efficient compression methods for storage. Traditional algorithms have low compression ratio, poor flexibility, and are unsuitable for storing large-scale remote sensing image data. To address this, we propose a remote sensing image lossy compression and storage method based on implicit neural representation. We used an implicit neural network to learn the mapping relationship between longitude, latitude coordinates, and values. Then, we compressed the network weights using quantization. Our method can significantly reduce storage space by approximately 80% for multi-source and heterogeneous remote sensing image data. And the reconstructed quality is capable of meeting the requirements of downstream tasks related to marine AI and visualization. © 2023 ACM.

关键词： remote sensing

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BFRNet: Bimodal Fusion and Rectification Network for remote sensing Semantic Segmentation 7th

BFRNet: Bimodal Fusion and Rectification Network for Remote ...

引用

7th Chinese conference on pattern recognition and Computer Vision

作者： Weng, Qian Lin, Yifeng Pan, Zengying Lin, Jiawen Chen, Gengwei Chen, Mo Zhang, Yingying Fuzhou Univ Coll Comp & Data Sci Fuzhou Peoples R China Fuzhou Univ Fujian Key Lab Network Comp & Intelligent Informa Fuzhou Peoples R China

ISBN: (纸本)9789819784929;9789819784936

In the semantic segmentation of high-resolution remote sensing images, utilizing the normalized Digital Surface Model (nDSM) that provides height information as auxiliary data and fusing it with the visible image can improve the accuracy of segmentation. However, the better utilization of complementarity between different modal features has not been fully explored. In this work, we propose a new dual-branch and multi-stage Bimodal Fusion Rectification Network (BFRNet), which is end-to-end trainable. It consists of three modules: Channel and Spatial Fusion Rectification (CSFR) module, Edge Fusion Refinement (EFR) module, and Multiscale Feature Fusion (MSFF) module. The CSFR module integrates and rectifies multimodal features in both channel and spatial dimensions, achieving sufficient interaction and fusion between multimodal features. The EFR module obtains better multiscale edge features than single modality through feature fusion based on bimodal interactive edge attention and spatial gate, which helps to alleviate the edge loss of ground objects in single modality. The MSFF module is used to upsample and fuse multiscale features from EFR and CSFR to generate the final semantic segmentation results. The experimental results on the two public datasets, Vaihingen and Potsdam, provided by ISPRS, showcase the comparative advantage of the proposed method over other research methods.

关键词： Semantic Segmentation Modal Fusion Feature Rectification remote sensing

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