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

共 3582 条记录，以下是231-240 订阅

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Comparison of State Vector Machine and Decision Tree - Content Based image Retrieval Algorithms to Perceive Accuracy 1

Comparison of State Vector Machine and Decision Tree - Conte...

引用

1st IEEE International conference on Innovations in High Speed Communication and signal processing, IHCSP 2023

作者： Kaur, Gagandeep Saini, Satish RIMT University Department of Electronics and Communication Engineering Punjab India

ISBN: (纸本)9798350345957

In recent years, there has been a significant amount of interest in Query By image Content retrieval (QBIC). CBIR is a technique for getting images comparable to the image given as a query from a huge database. In the context of image retrieval, CBIR is more similar to human semantics. In addition to medical imaging, surveillance, weather forecasting, crime prevention, and remote sensing, the CBIR technique is used in a variety of fields. This context refers to content as the visual information in images, like texture, shape, and color. In comparison to text-based image retrieval, image content contains more information for efficient retrieval. In this study, we used Python to implement CBIR algorithms such as SVM and DT and compared their accuracy. © 2023 IEEE.

关键词： Weather forecasting

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Building Change Detection Using Cross-Temporal Feature Interaction Network 48

Building Change Detection Using Cross-Temporal Feature Inter...

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48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Feng, Yuchao Jiang, Jiawei Xu, Honghui Zheng, Jianwei Zhejiang University of Technology China

ISBN: (纸本)9781728163277

Building change detection of remote sensing images is in full flourishing accompanied by the prosperity of convolutional neural networks. For spatial-temporal context modeling, existing solutions disregard the inter-image interactions, albeit their positive contribution to the acquisition of differences. To fill the gap, we propose a cross-temporal feature interaction network to effectively derive the change representations. Specifically, we propose a linearized cross-attention, which motivates each counterpart to glimpse the representation of another image while preserving its own features. In addition, to circumvent the misalignment caused by step-down sampling in the backbone, we introduce multi-level feature alignment using learnable affine transformation and stepwise aggregation. Based on a naive backbone (ResNet18) without sophisticated structures, our model outperforms other state-of-the-art methods on three datasets in terms of both efficiency and effectiveness. © 2023 IEEE.

关键词： Building change detection (BCD) convolutional neural network (CNN) linearized cross-attention multi-level feature alignment remote sensing (RS)

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Transmission and Control of the image Data from remote sensing Satellite to the Ground Station 8th

Transmission and Control of the Image Data from Remote Sensi...

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8th International conference on signal and Information processing, Network and Computers, ICSINC 2021

作者： Wang, Xiao-ming Wu, Xiao-dong Han, Bo Institute of Remote Sensing Satellite China Academy of Space Technology Beijing100094 China Xi’an Institute of Space Radio Technology Xi’an710071 China

ISBN: (纸本)9789811933868

The image data of remote sensing satellite are widely applied all over China and play an important role in economic and social development and ecological civilization construction. Based on the insufficient established ground station of China, the big data of remote sensing satellites should be processed suitably in orbit, compression and distribution are necessary, and be transmitted to the ground rightly and efficiently. The data quantity and the mode of the data transmission are analyzed. The composition and the mechanism of the data transmission system are introduced. The pointing control of antenna determines the rightness and efficiency of the transmission. Requirement of antenna control is analyzed and the way to weaken the disturbance from antenna is proposed. Meanwhile, the pointing control logic of antenna is described and the angle curve of antenna in orbit is given and analyzed. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Orbits

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Exploring Advanced image Refinement with Blind Denoising Autoencoder Architecture 2

Exploring Advanced Image Refinement with Blind Denoising Aut...

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2nd International conference on Recent Trends in Microelectronics, Automation, Computing, and Communications Systems, ICMACC 2024

作者： Reddy, V Sagar Reddy, G Venkata Viswas Mohan, K Pradhyuth Raj, A Chetan Sai VNRVJIET Dept of ECE Hyderabad India

ISBN: (纸本)9798350366570

This project introduces an innovative method for enhancing license plate images by employing a blind autoencoder-based denoising and deblurring technique. Unlike conventional approaches that heavily rely on labeled training data, which can be both scarce and costly to obtain, our method circumvents this requirement. Instead, it leverages a blind autoencoder trained on a vast collection of unlabeled license plate images to effectively denoise and deblur input images. Through evaluation on a standard dataset, our approach demonstrates significant enhancements in peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) compared to conventional methods. © 2024 IEEE.

关键词： Optical character recognition

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remote sensing Aircraft image Target Detection Based on Yolov5-EBA 7

Remote Sensing Aircraft Image Target Detection Based on Yolo...

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7th International conference on Vision, image and signal processing, ICVISP 2023

作者： Chen, Yunlong Jia, Yuan Zhang, Xiaodong School of Computer Science and Technology Southwest University of Science and Technology Sichuan Mianyang621000 China Troops Xi’an710000 China

ISBN: (纸本)9781839539848

The problems of overlooked detection and false detection issues are pervasive in the aircraft target detection process, and a remote sensing image target improvement model YOLOv5s-EBA based on YOLOv5s is proposed. first, the algorithm introduces an attention network to improve the model main network to enhance the feature selection of aircraft with different resolutions;then, the path aggregation network (PAN) in the original YOLOv5s network is replaced by the bidirectional feature pyramid network to implementing the multi-scale feature blending of aircraft targets simply and quickly;Finally, we substitute CIoU with α-IoU, and the prediction accuracy of the bounding box is improved by introducing the parameter α and by adjusting the parameter α. The experimental results indicate on the homemade dataset Airplane-GDD, contrast with the original YOLOv5 algorithm, the average precision of the YOLOv5s-EBA algorithm is increased by 2.2 percentage points, and the accuracy and recall witness a boost of 2.3 and 2.7 percentage points, respectively, and there is also a noteworthy enhancement for the problem of incorrect and overlooked detections in the detection process. © The Institution of Engineering & Technology 2023.

关键词： remote sensing

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SARViT: VISION TRANSFORMER FOR SAR image INTERPRETATION WITH EFFICIENT MODEL COMPRESSION FOR TIME-REAL processing

SARViT: VISION TRANSFORMER FOR SAR IMAGE INTERPRETATION WITH...

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IET International Radar conference 2023, IRC 2023

作者： Ma, Jian Zhang, Hao Wang, Zhengjue Wang, Zhibing Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China School of Electronic Engineering Xidian University Xiaan China

ISBN: (纸本)9781839539954

The synthetic aperture radar (SAR) images provide all-time and all-weather observation for the earth, which has become one of important techniques for remote sensing (RS). Recently, the large-scale vision transformers (ViT) have made significant progress for natural image processing due to their good scalability and representations. However, how to deploy the ViT for SAR image processing has not been explored. In this paper, we firstly resort to ViT with large-scale SAR images for model pretraining and then attempt to fine-tune the model tailored to downstream tasks for investigating how such large models perform. Moreover, in order to meet the requirement of online time-real processing on the satellite, we then develop a method of the knowledge distillation for model compression from 320M to 36M, which can significantly reduce the computational cost and memory footprint while reserve good representations. The experimental results of downstream tasks including scene classification, object detection and building segmentation show state-of-the-art performance compared to existing methods. Further experiments for compressed models also show the competitive results in terms of accuracy and computational complexity. © The Institution of Engineering & Technology 2023.

关键词： image compression

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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...

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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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Multi-domain Vision based Sign Language Recognition based on Auto Labeled Hand Tracking Data Learning 28

Multi-domain Vision based Sign Language Recognition based on...

引用

conference on image and signal processing for remote sensing XXVIII

作者： Lee, Junha Won, Hong-In Kim, Min Young Kim, Byeong Hak Kyungpook Natl Univ KNU 80 Daehakro Daegu South Korea Korea Inst Ind Technol KITECH 15 Jisiksanneop Ro Gyongsan South Korea

ISBN: (纸本)9781510655386;9781510655379

remote operating and autonomous systems are widely applied in various fields, and the development of technology for human machine interface and communication is strongly demanded. In order to overcome the limitations of the conventional keyboard and tablet devices, various vision sensors and state-of-the-art artificial intelligence image processing techniques are used to recognize hand gestures. In this study, we propose a method for recognizing a reference sign language using auto labeled AI model training datasets. This study can be applied to the remote control interfaces for drivers to vehicles, person to home appliances, and garners to entertainment contents and remote character input technology for the metaverse environment.

关键词： remote sensing human machine interface machine learning deep learning hand tracking sign language recognition multi-domain sensing object detection

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HETEROGENEOUS image CHANGE DETECTION BASED ON DEEP image TRANSLATION AND FEATURE REFINEMENT-AGGREGATION 30

HETEROGENEOUS IMAGE CHANGE DETECTION BASED ON DEEP IMAGE TRA...

引用

30th IEEE International conference on image processing (ICIP)

作者： Zhao, Tianrui Wang, Lu Zhao, Chunhui Liu, Tian Ohtsuki, Tomoaki Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Minist Ind & Informat Technol Key Lab Adv Marine Commun & Informat Technol Harbin Peoples R China Keio Univ Dept Informat & Comp Sci Keio Japan

ISBN: (纸本)9781728198354

remote sensing change detection (CD) has been widely studied, and the CD of heterogeneous images based on cross-sensor acquisition has significant research significance. However, the scarcity of heterogeneous data and the difficulty in obtaining high-quality change maps remain significant challenges. To address these issues, we propose a deep image translation-based feature refinement-aggregation change detection network (FRAN) designed for heterogeneous images, such as optics and SAR images. First, we use data augmentation to increase the number of available images and a no-independent-component-for-encoding GAN (NICE-GAN) to translate the features from the optical domain to the SAR image domain, enabling direct comparison of images from different domains. Finally, we introduce feature refinement module and feature aggregation module to extract more accurate change information and obtain an accurate change region. Our experiments on two public datasets demonstrate that the proposed FRAN's re-detection accuracy is superior to that of four other heterogeneous detection methods.

关键词： Change detection deep translation heterogeneous image feature refinement feature aggregation

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A Hierarchical Smoothing Method for Animation image Based on Scale Decomposition 7th

A Hierarchical Smoothing Method for Animation Image Based on...

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7th EAI International conference on Advanced Hybrid Information processing, ADHIP 2023

作者： Jiang, Jieling Li, Wei Guangzhou Academy of Fine Arts Guangzhou510006 China

ISBN: (纸本)9783031505485

In order to solve the problem of low quality of animated images affected by noise, a hierarchical smoothing method for animated images based on scale decomposition is proposed. Get the animation base image, and obtain the detail layer of the source image and target image. Use U-net convolutional neural network to select the decomposition box, select the results according to the remote sensing image segmentation box, and design the image decomposition process. Adjust the animation decomposition scale, focus on measuring multi-scale morphology, use the mean coordinate method to fuse the brightness of the target image, and retain rich details of the image. The fusion image smooth mosaic processing flow is designed, and the minimum variance standard is used to obtain the best matching combination. The gradient is used to represent the direction and size of the pixel changes in the animation image, and the details of the animation image are enhanced by means of superposition correction to achieve image edge smoothing. The experimental results show that the image details obtained by this method are consistent with the image samples, the signal-to-noise ratio is above 90 dB, and the longest smoothing processing time is 27 s, which can obtain high-quality animation images. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Animation

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