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conference on image and signal processing for remote sensing XXIX

作者： Rolland, Iain Selvakumaran, Sivasakthy Marinoni, Andrea Univ Cambridge Engn Dept Cambridge England UiT Arctic Univ Norway Tromso Norway

ISBN: (纸本)9781510666955;9781510666962

In the field of remote sensing it is common to have image data which can be considered in some way to be incomplete. This may relate to missing information caused by sensor failures, cloud cover or partially overlapping data acquisitions. In each of these cases it is of interest to consider how best this data can be completed. Whereas previous work has employed techniques such as low-rank tensor completion to tackle this problem, we present a graph-based propagation algorithm which diffuses entries around the incomplete image tensors. We show this approach is robust in even extreme circumstances for which large regions of image data are missing and compare the quality of our completions against the state of the art. In addition to improved performance as measured by reduced errors versus ground truth in experiments we also provide a comparison of our method's efficiency against benchmark methods and show that the approach is scalable as well as robust.

关键词： image reconstruction missing data tensor completion graph theory graph propagation remote sensing

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Speed Estimation Based on Inter-frame Difference

Speed Estimation Based on Inter-frame Difference

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

作者： Xie, Huawei Department of Forensic Science Fujian Police College Fuzhou China

ISBN: (纸本)9781510680012

Not all the physical evidence and traces left by field accidents are necessarily related to the occurrence of accidents and the identification of speed. Later appraisers also need to carry out the reconstruction of traffic accidents through the analysis of these evidence. Therefore, the traditional methods for the identification of speed of field road traffic accidents are generally complicated and not accurate enough. Therefore, Inter-frame Difference calculation method is more convenient, quick and accurate in the actual traffic accident case handling. © 2024 SPIE.

关键词： video signal processing

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Removal of thin clouds from high-resolution optical images based on multiscale feature fusion

Removal of thin clouds from high-resolution optical images b...

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2024 International conference on Advanced image processing Technology, AIPT 2024

作者： Xiao, Yunlong Hubei University of Technology Hubei Province Wuhan430000 China

ISBN: (纸本)9781510682542

High-resolution optical images are susceptible to atmospheric influences during their formation, and thin clouds are the most important influencing factor. Feature information loss due to thin-cloud coverage is a common problem. To solve this problem, a thin cloud removal method based on multi-scale feature fusion for high-resolution optical remote sensing images is proposed;this method can capture more image details. The multi-scale feature fusion module is introduced into the improved structure of the generative network model. By fusing multiple image scale information, the model can enhance the ability of extracting features such as shape, texture and colour. Experimental results on high-resolution remote sensing dataset show that the method achieves 29.4758 and 0.8902 for the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM), respectively. Comparing the method with existing methods, it proves that the method has a more accurate effect in remote sensing imagery thin cloud removal. © 2024 SPIE.

关键词： Optical remote sensing

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Learning and scoring Point Process models for object detection in satellite images 32

Learning and scoring Point Process models for object detecti...

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32nd European signal processing conference (EUSIPCO)

作者： Mabon, Jules Ortner, Mathias Zerubia, Josiane Univ Cote dAzur Inria Sophia Antipolis France Airbus Def & Space Toulouse France

ISBN: (纸本)9789464593617;9798331519773

In this paper we propose a joint Point Process and CNN based method for object detection in satellite imagery. The Point Process allows building a lightweight interaction model, while the CNN allows to efficiently extract meaningful information from the image in a context where interaction priors can complement the limited visual information. More specifically, we present matching parameter estimation and result scoring procedures, that allow to take into account object interaction. The method provides good results on benchmark data, along with a degree of interpretability of the output. The code will be available at ***/Ayana-Inria/

关键词： Object Detection Point Process Convolutional Neural Network Energy Based Model remote sensing

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Monitoring Technology and Data processing Methods for Chinese Bridges Research Progress

Monitoring Technology and Data Processing Methods for Chines...

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

作者： Chen, Xiaonian Chen, Cheng College of Civil Engineering Fuzhou University Fujian Fuzhou China

ISBN: (纸本)9781510680012

Since the number of bridges in China keeps increasing and some bridges with the damage problems brought by the age of are gradually taken into account, thus the health monitoring of bridges becomes much more important. Based on the research of China's intelligent sensing materials in the past decade, this paper summarizes the function of common sensing devices in crack pattern recognition, rust expansion monitoring, damage identification, curing shrinkage strain, and fatigue damage monitoring, and clarifies the feasibility of new monitoring technologies in practical engineering. It is also cited that the advantages and working principles of two data treatment, wavelet analysis and Long-term and short-term memory neural network, applied in the field of bridge health monitoring. The results show that intelligent sensing material and signal analysis technology has a wide application prospect in the future bridge surveillance work because of its good working performance. This paper can provide some reference for the application of intelligent sensing and signal analysis technology in the information of bridge health monitoring. © 2024 SPIE.

关键词： Wavelet analysis

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Analysis of the effects of heatwave events on continental surfaces from multi-spectral satellite series 7

Analysis of the effects of heatwave events on continental su...

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IEEE 7th International conference on Advanced Technologies, signal and image processing (ATSIP)

作者： Aicha, Chahbi Bellakanji Ferdaws, Ben Akacha Mehrez, Zribi Carthage Univ Natl Agron Inst Tunisia Lr17AGRO1 Lr GREEN TEAM 43 Av Charles Nicolle Tunis 1082 Tunisia Univ Toulouse CNES CNRS CESBIOUT3 Paul SabatierIRD F-31400 Toulouse France

ISBN: (纸本)9798350351491;9798350351484

Over the past few decades, Tunisia has faced heatwaves, droughts, and severe meteorological fluctuations, significantly impacting its agricultural sector and natural landscapes. This study focuses on the prolonged impact of heatwaves, with an emphasis on forests and pasture areas. To do so, multispectral remote sensing and deep learning techniques were employed to predict time series of NDvI (Normalized Difference vegetation Index) based on monthly maximum temperatures and monthly precipitation. The identification of heatwave periods relies on the use of predefined thresholds, the 95th and 99th percentiles, calculated through quantile analysis using daily maximum temperature data from 2000 to 2022, collected from 15 stations across the country. On average, it was found that there were 16 heatwave days each year;however, their occurrence varies depending on both time and geographical location. The study then assesses the impact of identified heatwaves on various land cover classes in distinct climatic contexts using MODIS sensor-derived Normalized Difference vegetation Index to calculate calibrated NDvI and vegetation Anomaly Index (vAI). This analysis highlights a significant correlation between heatwave events, NDvI, and monthly precipitation. Finally, a Long Short-Term Memory (LSTM) neural network model is developed to predict NDvI time series. The results underscore the importance of the unique characteristics of each land cover type, such as dense forests, open forests, pastures in subhumid bioclimate, and pastures in arid bioclimate, in their ability to respond to extreme climate variations.

关键词： Heatwave MODIS Deep Learning Neural Network vegetation Dynamics NDvI remote sensing

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SlotFusion: Object-Centric Audiovisual Feature Fusion with Slot Attention for remote sensing Scene Recognition

SlotFusion: Object-Centric Audiovisual Feature Fusion with S...

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2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Han, Fangzhou Yu, Tianyi Zhang, Lamei Si, Lingyu Zhang, Yiqi Dept. of Information Engineering Harbin Institute of Technology Harbin China National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China Institute of Software Chinese Academy of Sciences Beijing China

ISBN: (纸本)9798350368741

Despite significant advancements in remote sensing multimodal learning, particularly in image-image feature fusion, the exploration of audio-image feature fusion remains insufficient. Given the complexity and redundancy of ground objects in remote sensing images, accurately aligning audio features with image features during the fusion process is a critical challenge. In this paper, we introduce an object-centric feature fusion method named SlotFusion. By employing a slot attention-based feature decoupling module and a slot-based audiovisual feature fusion module, we transform modality features with complex semantic information into a set of slot features corresponding to object units and use gated activation units to adaptively implement object-centric feature fusion. Experiments on the Audio visual Aerial Scene Recognition dataset (ADvANCE) demonstrate that the proposed SlotFusion significantly improves remote sensing scene recognition performance, with a 7.04% increase in overall accuracy compared to previous methods, achieving state-of-the-art results. © 2025 IEEE.

关键词： Audiovisual feature fusion Gated feature fusion Object-centric learning Slot attention

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Comparative Analysis of Pre-trained Architectures for remote sensing image Scene Classification 1

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International conference on Advances in signal processing and Communication Engineering, ICASPACE 2023

作者： Atchaya, A. Josephine Priya, Asha Gnana Pranesh, M. Anitha, J. Department of ECE Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (数字)9789819705627

ISBN: (纸本)9789819705610

remote sensing offers a comprehensive perspective and a practical overview of a region. Through remote sensing, we can monitor the physical characteristics of an area by measuring the radiation reflected and emitted remotely. In most cases, this is done by satellite or aircraft (It can be a drone image). It is possible to interpret a lot of information using remote sensing (RS). There is still a challenge in selecting and combining appropriate features from remote sensing imagery based on their spectral and spatial characteristics. A deep artificial neural network is a computing system that has the capacity to learn from data on its own using algorithms. In scene classification approaches, convolutional neural networks have been successfully applied to create a system capable of handling classification without human intervention. The pre-trained classification technique is suggested in this work to categorize the satellite images. The classifiers are implemented for pre-trained deep learning networks including AlexNet, Resnet50, GoogleNet, vgg16, and Inception v3. The performance is measured in terms of classification accuracy (AC), sensitivity (SE), specificity (SP), and positive predictive value (PPv). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： remote sensing

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A novel CNN-Transformer network for cloud detection in remote sensing image

A novel CNN-Transformer network for cloud detection in remot...

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

作者： Ai, Xinkai Sun, Lin College of Geodesy and Geomatics Shandong University of Science and Technology Shandong Province Qingdao266590 China

ISBN: (纸本)9781510680425

Convolutional neural networks (CNNs) are the mainstream model for extracting rich features in deep learning-driven studies on cloud detection for remote sensing images. However, due to the limitation of receptive fields in convolutional operations, a single CNN has limitations in mining global information and long-range dependencies, which affects the precision of cloud identification in intricate environments. To address the insufficient global feature and long-range dependency modeling of CNNs, a new CNN-Transformer network for cloud detection (CNN-TFCD) in remote sensing images is proposed in this paper. This method comprehensively utilizes strengths of both CNN and transformer to fully mine local spatial details, global contextual information, and long-range dependencies, to obtain comprehensive feature representations. The feature cascade mechanism is applied for multi-scale feature fusion, further enhancing the model's feature representation capability. Evaluations conducted on the Landsat-8 dataset demonstrate that the CNN-TFCD method achieves accurate and stable cloud detection. © 2024 SPIE.

关键词： Convolutional neural networks

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SAR image formation scheme implementation and endorsement sprouting from Level-0 data decoding

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EGYPTIAN JOURNAL OF remote sensing AND SPACE SCIENCES 2023年第2期26卷 253-263页

作者： Azouz, Ahmed Fouad, Mohamed ELbohy, Ahmed Abdalla, Ahmed Mashaly, Ahmed Mil Tech Coll Elect Engn Branch Cairo Egypt

Synthetic Aperture Radar (SAR) data processing evolving from level-0 raw data is complicated, especially in data decoding, manifesting in obtaining a well-focused SAR image. This paper is intended to present a complete MATLAB-based SAR data processing tool, which helps the end-user to treat simply the steps of image generation. This paper would enrich the research community in the field of SAR processors, especially in the area of understanding, handling, and developing a SAR processor based on space packet protocol standard (STD 01) used in many SAR systems such as Sentinel-1, ERS-1, CubeL, JPSS-2, 3, and 4. Also, this work opens the door for researchers to decode other space packet protocol standards and even to create an algorithm based on fully understanding the image formation algorithm from its roots. Moreover, the work in this paper could be a stepping-stone for the beginner in the field of SAR signal processing to become familiarized with SAR image generation procedures. The level-0 raw data used in this paper is based on Sentinel 1 SAR satellite obtained from the European Space Agency Copernicus website, a free open-source for level-0 and level-1 data types. The MATLAB program allows users to compare their generated image with the level-1 single-look complex (S1-L1-SLC) image utilizing entropy, contrast, and sharpness image quality metrics. The results showed that the images produced by the proposed algorithm are comparable to Sentinel-1 level-1 SAR images for the same scene and achieved satisfactory accuracy under the requirements for image quality. (c) 2023 National Authority of remote sensing & Space Science. Published by Elsevier B.v. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Unpacking Decoding Synthetic Aperture Radar SAR Chirp scaling algorithm CSA Sentinel-1 Level-0 Level-1 Space packet protocol

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