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

共 3573 条记录，以下是221-230 订阅

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Feature Enhancement Method for Text Information Generation of remote sensing images 4

Feature Enhancement Method for Text Information Generation o...

引用

4th International conference on Intelligent Control, Measurement and signal processing, ICMSP 2022

作者： Lin, Qihui Li, Sen Xia, Lurui Shan, Yihe Space Engineering University Beijing China

ISBN: (数字)9781665486583

ISBN: (纸本)9781665486583

The redundancy of irrelevant information brings great inconvenience to the interpretation of remote sensing images. image captioning can efficiently extract effective information and eliminate irrelevant data to the greatest extent. However, inadequate use of visual features is a common problem in the current research on remote sensing image captioning. To solve this problem, we propose two visual feature enhancement methods for remote sensing image captioning, which are the combination of multi-scale features and the use of remote sensing images pre-training weights. Experiments are carried out using different language models, and the results prove the effectiveness of our proposed feature enhancement methods. © 2022 IEEE.

关键词： image enhancement

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Two-step registration of near-space remote sensing images via deep neural networks 28

Two-step registration of near-space remote sensing images vi...

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conference on image and signal processing for remote sensing XXVIII

作者： Li, Xiaohan An, Meng Zhang, Haopeng Xie, Fengying Beihang Univ Sch Astronaut Dept Aerosp Informat Engn Beijing 102206 Peoples R China CAST Inst Spacecraft Syst Engn Beijing 100094 Peoples R China Beijing Key Lab Digital Media Beijing 102206 Peoples R China Minist Educ Key Lab Spacecraft Design Optimizat & Dynam Simul Beijing 102206 Peoples R China

ISBN: (纸本)9781510655386;9781510655379

Near-space remote sensing image registration is an important foundation of near-space image processing. For large image jitter distortion, geometric and atmospheric distortion of its image, we propose a two-step method based on deep neural networks, which includes a coarse-to-fine registration process. We construct a near-space image registration dataset which is captured from Google Maps and hot air balloon platforms, etc. For obtaining candidates, the coarse alignment stage applies classical geometric validation methods to a corresponding set of pre-trained deep features. The fine alignment network is based on pyramidal feature extraction and optical flow estimation to realize local flow field inference from coarse to fine. We construct a regularization layer for each level to ensure smoothness. Applying our method to our synthetic dataset, the experimental result shows that it has a competitive result that is evaluated based on the root mean square error, peak signal to noise ratio and structural similarity.

关键词： Near-space remote sensing image image registration self-supervised convolutional neural networks

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Optimization and Application of Lucy-Richardson Filter on Optical remote sensing image Restoration 8th

Optimization and Application of Lucy-Richardson Filter on Op...

引用

8th China High Resolution Earth Observation conference, CHREOC 2022

作者： Wang, Yunsen Ma, Chengzhi Wu, Panfeng Li, Weiqiang Song, Qi Shandong Institute of Space Electronic Technology Shandong Yantai264670 China

ISBN: (纸本)9789811982019

In complex space environment, remote sensing imaging process might be affected by different kinds factors. Lucy-Richardson (L-R) filter method is an extensive used method in image reconstruction field. In order to decrease the calculation time and promote the quality of image, a optimal semi-blind remote sensing data reconstruction algorithm based on improved L-R filter is presented. Firstly, self-correlation function is innovated to calculate the point spread function (PSF). To suppress the superposition of noise in L-R iterative process, both full-reference and no-reference evaluation parameters are applied in iteration termination conditions. Finally, Gaussian filter is adopted to filtrate low-pass signal when the noise is constantly amplified. Experiments and simulation results explanation that the improved Lucy-Richardson filter algorithm could gain relatively satisfactory reconstruction effects. And the proposed processing method could be used for the restoring and reconstruction of those remote sensing data degraded by rectilinear motion and random noise interference. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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CSVD: a cross-scenario vehicle dataset for multi-object tracking

CSVD: a cross-scenario vehicle dataset for multi-object trac...

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2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Li, Xiaolei Zhou, Juefan Xiao, Xingjie Lin, Jiayu Yang, Siyuan Sha, Zongyao Tu, Jianguang School of Remote Sensing and Information Engineering Wuhan University Hubei Province 430079 China Wuhan ALLPRS Remote Sensing Data Technology Co. Ltd. Hubei Province 430079 China

ISBN: (纸本)9781510680425

Leveraging visual sensing technologies for the detection and tracking of vehicles represents a critical application domain for unmanned aerial vehicles (UAVs), notably in challenging operational *** study focuses on enhancing UAV functionalities in intricate environments through the development of a specialized dataset, derived from battlefield scenarios, to facilitate advanced research on vehicle detection and multi-target tracking under complex conditions.A comprehensive collection of vehicular movement videos spanning diverse scenarios was amassed and manually annotated, culminating in the creation of the " Cross-Scenario Vehicle Detection" (CSVD) *** dataset encompasses a wide array of environmental settings, featuring urban landscapes, plains, and forests, across the four seasons, resulting in a total of 13,025 meticulously annotated *** several state-of-the-art deep learning models, we established robust benchmarks for object ***, an extensive evaluation and performance validation were conducted using cutting-edge multi-object tracking algorithms on the CSVD dataset, incorporating diverse assessment *** conducted experiments demonstrate the dataset's robust applicability and versatility, endorsing its effectiveness for the development and evaluation of UAV-based vehicle detection and multi-target tracking systems in complex settings. © 2024 SPIE.

关键词： Aircraft detection

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Exploring the Potential of Ultra-Low Latency Spiking Neural Network in remote sensing image Classification

Exploring the Potential of Ultra-Low Latency Spiking Neural ...

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IET International Radar conference 2023, IRC 2023

作者： Li, Jiahao Xu, Ming Chen, He Li, Can Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781839539954

With the rapid advancement of artificial intelligence, the depth of artificial neural networks (ANN) has increased, resulting in higher power consumption and resource utilization. To address these concerns, researchers have begun to explore spiking neural networks (SNN) as an alternative. SNN offers the potential for improved energy efficiency while achieving comparable performance. In recent years, there has been a growing interest in applying SNN to remote sensing classification scenarios. However, one major challenge hindering its widespread adoption is the high latency associated with real-time processing. To address this challenge, we adopt a novel hybrid training approach that combines ANN-SNN conversion with backpropagation through time. We evaluate the effectiveness of our approach on three publicly available datasets: MSTAR, AID and UCMerced. Our experiment results demonstrate that the proposed ultra-low latency SNN achieves classification accuracy rates of 99.05%, 91.36%, and 92.62%, respectively, with a single time step. To the best of our knowledge, this is the first time to apply ultra-low latency SNN to remote sensing image classification tasks. Our findings suggest that this innovative approach has significant potential for future research in the deployment of these models for on-board processing. © The Institution of Engineering & Technology 2023.

关键词： image classification

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Adaptive Compressive sensing for Natural images Using Schur Decomposition and Optimized Reconstruction 4

Adaptive Compressive Sensing for Natural Images Using Schur ...

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4th International conference on Sustainable Expert Systems, ICSES 2024

作者： Elaveini, M. Aarthi Deepa, T. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Electronics and Communication Engineering Ramapuram Chennai India

ISBN: (纸本)9798331540364

Compressive sensing (CS) is growing as an effective method for efficient imagine capture and recovery by harnessing the fundamental sparseness of natural images. The paper presents a unique framework for CS of live images employing Schur decomposition, a matrix decomposition approach that improves the detection and reconstruction processes. The suggested technique utilizes Schur decomposition to enhance the sensing matrix, facilitating more effective sampling by collecting essential imagine information in both spatial and frequency domains. The Schur-based sensing matrix, in contrast to conventional random sensing matrices, is designed to use the structural characteristics of real pictures, hence enhancing the precision of image reconstruction from a reduced quantity of observations. The integrated Schur decomposition with an iterative image reconstruction algorithm, incorporating Orthogonal Matching Pursuit (OMP) promotes sparsity. The combination improves the robustness of the recovery process while reducing reconstruction artifacts. Extensive simulations demonstrate that proposed Schur-based framework achieves superior performance compared to conventional CS techniques, particularly in terms of Peak signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). The proposed method improves the quality of reconstructed images and expedites the convergence of the reconstructing technique, hence reducing computational complexity. The framework is assessed over several natural imagine datasets, demonstrating substantial improvements in image quality and computing efficiency resulting in a PSNR of 37.263 dB and SSIM 0.813 for CAT image. The Schur decomposition method for CS is an effective solution for applications necessitating quick picture capture and high-quality reconstruction, including medical imaging, remote sensing, and real-time image processing systems. © 2024 IEEE.

关键词： image reconstruction

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2023 8th International conference on Frontiers of signal processing, ICFSP 2023

2023 8th International Conference on Frontiers of Signal Pro...

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8th International conference on Frontiers of signal processing, ICFSP 2023

ISBN: (纸本)9798350308792

The proceedings contain 21 papers. The topics discussed include: pornography image detection in digital forensics;poverty areas detection and mapping through combination of remote sensing and machine learning: a case study of ORAN, ALGERIA;viscosity and density estimation using centrifugal pump mechanical vibration and neural networks;single OTRA based universal third order Butterworth filter and its performance study;hybrid QKD &PQC protocols implemented in the Berlin OpenQKD testbed;image size reduction by controlled amount of band limitation according to coding difficulty;speech denoising based on wavelet transform and wiener filtering;two-dimensional circular signal systems;speech enhancement using binary estimator selection applied to hearing aids with a remote microphone;and spoken digit classification through neural networks with combined regularization.

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Research on Semantic Segmentation Algorithm of Multispectral remote sensing image Based on Deep Learning 2

Research on Semantic Segmentation Algorithm of Multispectral...

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2nd International conference on signal image processing and Communication, ICSIPC 2022

作者： Li, Zhang Yibei, Chen Yiyong, Lin Bingxi, Dong Xinglong, Ran Xichang Satellite Launch Center Sichuan Xichang615000 China

ISBN: (纸本)9781510657694

The semantic segmentation technology of remote sensing image refers to labeling the semantic information of pixel-level of the image to complete the classification, namely, terrain classification. It is widely used in intelligent maps, smart cities and other aspects. With the increase of satellite image resolution and the development video communication moonlet, its application and scenes are greatly broadened. The traditional remote sensing image semantic segmentation method mainly uses statistical machine learning methods, which cannot take into account the spectral features and the context semantic relationship of pixels, and has a bottleneck in improving the accuracy of classification. In deep learning method, Using the convolutional neural networks to extract features can achieve classification results of remote sensing images with higher classification accuracy. Aiming at the problems of existing deep learning methods in multi-spectral image semantic segmentation, to make full use of the information of multi-spectral images, this paper proposes a semantic segmentation algorithm for multispectral remote sensing images based on deep learning and verified the method on open data sets. © 2022 SPIE.

关键词： Semantic Segmentation

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MULTILAYER ATTENTION MECHANISM FOR CHANGE DETECTION IN SAR image SPATIAL-FREQUENCY DOMAIN 30

MULTILAYER ATTENTION MECHANISM FOR CHANGE DETECTION IN SAR I...

引用

30th IEEE International conference on image processing (ICIP)

作者： Ma, Lirui Wang, Lu Zhao, Chunhui Jiahui, E. Ohtsuki, Tomoaki Harbin Engn Univ Coll Informat & Commun Engn Harbin Peoples R China Key Lab Adv Marine Commun & Informat Technol Harbin Peoples R China Keio Univ Dept Informat & Comp Sci Minato Japan

ISBN: (纸本)9781728198354

Change detection based on synthetic aperture radar (SAR) images is a challenging task in the field of remote sensing image analysis due to the influence of noise and the lack of labeled data. In this paper, we propose a new unsupervised change detection algorithm based on deep learning, which explores the spatial and frequency domain features of SAR images in parallel to improve detection performance. Our proposed method first obtains pseudo-labels by clustering and then combines them with neural networks for unsupervised detection. To reduce the impact of noise and improve sensitivity to changes, we integrate an attention mechanism (AM) into the network. We also use complementary features to integrate the spatial and frequency domain features. These complementary features include a multi-regional feature weighted by channel-spatial AM and a deep feature filtered out by a gated linear unit (GLU). Experimental results demonstrate that the proposed method improves the detection accuracy.

关键词： SAR image change detection spatical-frequency domain attention mechanism gated linear unit

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Cloud Cover Removal from remote sensing Data using GANs Based on Attention Mechanism 7

Cloud Cover Removal from Remote Sensing Data using GANs Base...

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7th International conference on image Information processing, ICIIP 2023

作者： Siva Jyothi Natha Reddy, B. Sasikala, D. School of Computing Computer Science And Engineering Amrita Vishwa Vidyapeetham Chennai India

ISBN: (纸本)9798350371406

Cloud interference is a significant challenge in remote sensing applications, impacting the quality and reliability of data used for environmental analysis, disaster monitoring, and urban planning. In this study, we present an innovative approach to remove clouds from remote sensing data by employing Generative Adversarial Networks (GANs) with an integrated attention mechanism. Our method is designed to effectively restore regions obscured by clouds while preserving the integrity of the remaining image. We introduce a specialized architecture called U-net based Attention GAN (UAGAN) to address cloud cover issues. In this architecture, the generator network utilizes attention mechanisms to identify and rectify cloud-affected areas. The attention mechanism dynamically emphasizes relevant spatial regions during cloud removal, enhancing the precision of the restoration process. To evaluate our approach, we conducted experiments using the RICE dataset and trained our UAGAN model. Our method's effectiveness was assessed using quantitative metrics, including the Structural Similarity Index (SSIM), Root Mean Squared Error (RMSE), and Peak signal-to-Noise Ratio (PSNR). Comparative analysis of three different models, namely vanilla GAN, U-net GAN, and UAGAN, demonstrates that UAGAN outperforms the others with impressive results, achieving a PSNR of 29.901 dB, RMSE of 7.891, and SSIM of *** research contributes to the advancement of cloud removal techniques in remote sensing, offering a promising solution for improving data quality and enhancing the accuracy of downstream applications. The integration of GANs and attention mechanisms showcases the potential for innovative approaches to address complex challenges in remote sensing data analysis. © 2023 IEEE.

关键词： Generative adversarial networks

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