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

共 3583 条记录，以下是491-500 订阅

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Seeing the World Through an Antenna's Eye: Reception Quality Visualization Using Incomplete Technical signal Information

Seeing the World Through an Antenna's Eye: Reception Quality...

引用

European signal processing conference (EUSIPCO)

作者： Leif Bergerhoff German Aerospace Center (DLR) German Remote Sensing Data Center (DFD) National Ground Segment (NBS) Neustrelitz Germany

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

We come up with a novel application for image analysis methods in the context of direction dependent signal characteristics. For this purpose, we describe an inpainting approach adding benefit to technical signal information which are typically only used for monitoring and control purposes in ground station operations. Recalling the theoretical properties of the employed inpainting technique and appropriate modeling allow us to demonstrate the usefulness of our approach for satellite data reception quality assessment. In our application, we show the advantages of inpainting products over raw data as well as the rich potential of the visualization of technical signal information.

关键词： Satellites image analysis Data visualization Europe signal processing Data models Quality assessment Monitoring Antennas

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A Deep Learning-Based Methodology for Detecting and Visualizing Continuous Gravitational Waves 19th

A Deep Learning-Based Methodology for Detecting and Visualiz...

引用

19th International conference on Artificial Intelligence Applications and Innovations (AIAI)

作者： Pintelas, Emmanuel Livieris, Ioannis E. Pintelas, Panagiotis Univ Patras Dept Math Patras Greece

ISBN: (纸本)9783031341137;9783031341113;9783031341106

Since the Gravitational Waves' initial direct detection, a veil of mystery from the Universe has been lifted, ushering a new era of intriguing physics, astronomy, and astrophysics research. Unfortunately, since then, not much progress has been reported, because so far all of the detected Gravitational Waves fell only into the Binary bursting wave type (B-GWs), which are created via spinning binary compact objects such as black holes. Nowadays, as-tronomy scientists seek to detect a new type of gravitational waves called: Continuous Gravitational Waves (C-GWs). Unlike the complicated burst na-ture of B-GWs, C-GWs have elegant and much simpler form, being able to provide higher quality of information for the Universe exploration. Never-theless, C-GWs are much weaker comparing to the B-GWs, which makes them considerably harder to be detected. For this task, we propose a novel Deep-Learning-based methodology, being sensitive enough for detecting and visualizing C-GWs, based on Short-Time-Fourier data provided by LIGO. Based on extensive experimental simulations, our approach significantly outperformed the state-of-the-art approaches, for every applied experimental configuration, revealing the efficiency of the proposed methodology. Our expectation is that this work can potentially assist scientists to improve their detection sensitivity, leading to new Astrophysical discoveries, via the incor-poration of Data-Mining and Deep-Learning sciences.

关键词： Deep learning 2D image processing SFT signal processing remote sensing applications ML methods for sensing systems

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MDFNet: Multimodal Feature Decomposition and Fusion Network for Multimodal remote sensing image Semantic Segmentation

MDFNet: Multimodal Feature Decomposition and Fusion Network ...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Tianyu Wei He Chen Jue Wang Wenchao Liu National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The semantic segmentation of multimodal remote sensing (RS) images, utilizing optical and synthetic aperture radar (SAR) data, has raised attention in recent studies. Advanced studies adaptively modeled and fused modality-share and modality-specific information beneficial for segmentation and achieved competitive performance. However, in challenging scenarios such as low image contrast or blurred textures in optical images and speckle noises or foreshortening in SAR images, modality-specific information may lost. This study proposed a multimodal feature decomposition and fusion network (MDFNet) designed for multimodal RS image semantic segmentation using optical and SAR images. By decomposing multimodal features into modality-share and modality-difference features and applying gradient descent on modality-difference features, modality-specific information beneficial for segmentation can be retained. Specifically, we designed a MDF decoder with MDF blocks. MDF block maps multimodal features into the same feature space and calculates the difference of multimodal features to obtain modality-share and modality-difference features respectively, then fuses these features. MDF decoder optimizes modality-difference features by gradient descent using adaptive modeling, thereby retaining modality-specific information that is beneficial for segmentation. Comprehensive experiments conducted on the DFC20 dataset demonstrated that the proposed MDFNet surpasses representative methods.

关键词： Adaptation models Semantic segmentation Optical fiber networks Optical imaging Adaptive optics Radar polarimetry Decoding Optical sensors remote sensing Synthetic aperture radar

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Rayleigh Distribution-based Edge Detection in SAR images 10

Rayleigh Distribution-based Edge Detection in SAR Images

引用

10th International conference on Emerging Trends in Engineering and Technology - signal and Information processing, ICETET-SIP-22 2022

作者： Paul, Sourabh Saiteja, B. Rajasekharan, S. Pravallika, K. Prudhvi Reddy, K. Electronics and Communication Engineering Madanapalle India

ISBN: (纸本)9781665467414

Edge detection in Synthetic Aperture Radar (SAR) images is challenging task in remote sensing as the images contain significant speckle noise. Many ratio-based edge detectors have been developed in recent years to effectively identify the edges in SAR images. However, an automatic threshold value selection in edge detection is a critical issue. In this paper, a Rayleigh distribution-based edge detection method is proposed for the automatic selection of threshold value. This method is very effective to automatically identify the true edges and reject the false edges. Experiments are performed on different SAR images to verify the effectiveness of the developed method. © 2022 IEEE.

关键词： image edge detection Information processing Detectors Speckle Market research Radar polarimetry Task analysis

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A Fast Saturation Based Dehazing Framework with Accelerated Convolution and Attention Block

A Fast Saturation Based Dehazing Framework with Accelerated ...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shuocheng Wang Jiaming Liu Yilian Zhong Ruoxi Zhu Jiazheng Lian Hao Zhang Yibo Fan State Key Laboratory of Integrated Chips and Systems Fudan University

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Real-time image dehazmg is crucial for applications such as autonomous driving, surveillance, and remote sensing, where haze can significantly reduce visibility. However, many deep learning algorithms are hindered by large model sizes, making real-time processing difficult to achieve. Several fast and lightweight dehazing networks rely on estimating K(x), but they often fail to deliver satisfying performance. In this paper, we present a novel fast dehazing framework built upon the saturation-based algorithm. We design a new convolution module called Feature Extraction Partial Convolution (FEPC), which is faster and achieves better performance than the vanilla 3×3 convolution. Additionally, we fully leverage the information redundancy between feature map channels by dividing it into two parts along the channel dimension and designing a Self-Cross Attention Block (SCAB). The reduction in channel count significantly reduces computational load and improves the framework’s speed. Through extensive experiments, our method demonstrates not only a fast inference speed but also superior dehazing performance, providing a promising solution for real-time practical deployment. Our code will be available at https://***/superwscZFSB-Dehazing-Framework.

关键词： Deep learning image quality Convolution Surveillance Redundancy signal processing algorithms Feature extraction Real-time systems Speech processing remote sensing

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Atmospheric image Correction and Removal of Cloud Cover for Satellite images

Atmospheric Image Correction and Removal of Cloud Cover for ...

引用

2023 International conference on Sustainable Computing and Smart Systems, ICSCSS 2023

作者： Kharat, Vaishnavi Khatdeo, Sanyukta Kothe, Harshada Kshirsagar, Rutuja Dixit, Mrudul Selva Balan, M. MKSSS's Cummins College of Engineering for Women Electronics & Telecommunication Pune India Hydraulic Instrumentation Pune India

ISBN: (纸本)9798350333602

Accurate atmospheric correction is crucial for surface analysis and environmental monitoring. Adverse weather conditions, such as clouds and haze, often degrade the contrast and fidelity of outdoor images, leading to uncertainties in surface observations. Cloud removal techniques in computer vision and image processing, including recent approaches like Generative Adversarial Networks (GANs), image Inpainting, and image Fusion, have been developed to address this challenge. However, these techniques face various challenges, such as limited availability of diverse training data, artifacts introduced in the resulting images, and computational complexity. This paper focuses on the removal of cloud cover and haze in multiband satellite images using the S2 Cloudless dataset and the Dark Channel Prior (DCP) algorithm. An approach is proposed to reconstruct clouded areas and generate cloud-free spatio-temporally continuous images for in-depth time-series analysis. The objective is to enhance the utility of the data by restoring the polluted areas in cloudy satellite imagery. The methods are evaluated using metrics such as Mean Square Error (MSE), Peak signal-to-Noise Ratio (PSNR), Structural Similarity Index Measure (SSIM), and Average Mean Pixel Value to assess the performance of both the input and output images. The results demonstrate the effectiveness of the methods in improving the quality and utility of cloud-covered satellite imagery, overcoming the challenges posed by limited training data, artifacts, and computational complexity. © 2023 IEEE.

关键词： remote sensing

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remote sensing image Classification Methods Based on CNN: Challenge and Trends

Remote Sensing Image Classification Methods Based on CNN: Ch...

引用

2021 International conference on signal processing and Machine Learning, CONF-SPML 2021

作者： Yuan, Liyao Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665417341

remote sensing image classification occupies a vital place in earth observation and has many applications in military and civil fields. It can be divided into two typical tasks: high-resolution remote sensing images and hyperspectral image classification. However, high-resolution remote sensing and hyperspectral image classification cannot facilitate all features and achieve good accuracy with traditional methods. As deep learning methods, especially the convolutional neural networks (CNN), are developing rapidly, image classification methods based on CNN can perform well and provide new ideas for remote sensing classification. In this paper, we first review the background of typical remote sensing images and CNN. Then, we provide an overview of the development of the CNN model. After that, we point out some existing problems that we need to overcome for the CNN methods. Finally, the corresponding solutions are provided, and future work is presented with the analysis of some popular methods. © 2021 IEEE.

关键词： image classification

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Super-Resolution Reconstruction of remote sensing images Based on Improved Residual Dense Network

Super-Resolution Reconstruction of Remote Sensing Images Bas...

引用

image processing and Computer Applications (ICIPCA), IEEE International conference on

作者： Zhang Pengying Zhang Ming Li Jianjun Zhang Baohua Inner Mongolia University of Science and Technology College of Information Engineering Baotou China

An improved residual dense network (IRDN)-based remote sensing images super-resolution reconstruction technique is suggested in order to address the issues with single feature extraction and equalization of feature processing in the current remote sensing images super-resolution reconstruction algorithm. By strengthening the structure of the original residual dense model, adding a branch to extract more varied and rich features, and establishing the channel and position relationship of features by Coordinate Attention (CA), the algorithm enhances the performance of the model's feature extraction and representation. The strategy can effectively increase the reconstruction performance while significantly lowering the number of model parameters, according to experiments using the publicly accessible remote sensing datasets UC MERCED and NWPU-RESISC45. On the UC MERCED dataset with a scale factor of 4, the objective performance evaluation indexes of peak signal to noise ratio and structure similarity achieve 29.3126dB and 0.7987, respectively, and good visual effects are obtained. It offers a solid foundation for the future remote sensing images' additional analysis and processing.

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Experimental Results of Local Backprojection for Monostatic THz SAR Imaging

Experimental Results of Local Backprojection for Monostatic ...

引用

2025 International conference on Mobile and Miniaturized Terahertz Systems, ICMMTS 2025

作者： Ivanenko, Yevhen Batra, Aman Vu, Viet Thuy Pettersson, Mats I. Kaiser, Thomas Blekinge Institute of Technology Karlskrona371 79 Sweden University of Duisburg-Essen Duisburg470 57 Germany

ISBN: (纸本)9798350365832

The use of THz frequencies has enabled the opportunity to perform synthetic-aperture radar (SAR) imaging at the sub-mm level. It is of great interest for applications where high-resolution remote sensing in short range is required. However, with the increase of the operating frequencies from microwave to THz, the SAR image formation algorithms work with a larger amount of data and become more sensitive to phase errors that can be caused by insufficient signal sampling rate or physical factors that cause platform deviations. This motivates the use of fast image formation capable to handle phase errors. In this paper, we present the experimental results on the performance of the local backprojection (LBP) algorithm for processing THz SAR signals. The LBP algorithm has been tested with the real data in the frequency range 0.325-0.5 THz. The results demonstrate the efficiency of the LBP algorithm for the SAR scene reconstruction and highlight the necessity of the use of two times higher signal upsampling to achieve reconstruction accuracy similar to the global backprojection algorithm. © 2025 IEEE.

关键词： image sampling

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A Variational Approach for Robust Online Fusion of Multiresolution Multispectral images

A Variational Approach for Robust Online Fusion of Multireso...

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European signal processing conference (EUSIPCO)

作者： Haoqing Li Ricardo Borsoi Tales Imbiriba Pau Closas Dept. of Geomatics Engineering University of Calgary Calgary Alberta Canada CNRS CRAN University of Lorraine Nancy France Dept. of Electrical & Computer Engineering Northeastern University Boston MA USA Institute for experimental AI Northeastern University Boston MA USA

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Multi-resolution image fusion is a key problem for real-time satellite imaging, which has a central role in detecting and monitoring the intensity of key natural phenomena such as floods. It aims to solve the trade-off between high temporal and high spatial resolution in remote sensing instruments. Although several algorithms have been proposed to solve this problem, the presence of outliers caused by, e.g., cloud cover downgrades their performance. In this paper, an online image fusion method based on a robust Kalman filter with a weakly supervised approach for temporal dynamics estimation is proposed. Outliers are modelled as a discrete variables, where the probability of contamination for each pixel and spectral band is modelled by a latent variable whose distribution is approximated under variational inference. Experiments fusing images from the MODIS and Landsat 8 sensors show that the proposed approach is significantly more robust against cloud cover, without losing its efficiency when no clouds are present.

关键词： Satellites signal processing algorithms Real-time systems Sensors Kalman filters Spatial resolution image fusion signal resolution remote sensing Multiresolution analysis

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