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27th International Conference on pattern recognition, ICPR 2024

作者： Chen, Dongdong Ju, Rusheng Liu, Xiaoyang Liu, Jiyuan Zhang, Jun Qiu, Sihang College of Systems Engineering National University of Defense Technology No. 109 Deya Road Kaifu District Changsha China

ISBN: (纸本)9783031781681

Ship detection in remote sensing images has a wide range of research needs in both military and civilian applications. Traditional object detection algorithms rely solely on optical images as input, which are susceptible to various interferences such as cloud and fog occlusion, scene noise, etc., resulting in unsatisfactory detection accuracy. Based on the YOLO framework, this paper designs a ship detection model that integrates visible light and infrared images through dual-modal fusion, and explores various feature fusion methods. It achieves data-level fusion network SwinFuse-Ynet, feature-level fusion network E-Dualswin-Ynet and decision-level fusion network L-Dualswin-Ynet. Comprehensive validations are conducted on the multispectral satellite image datasets MMShip and VI-ship. The experimental results demonstrate that the three proposed networks effectively complement infrared feature information, reduce interference from complex scene factors, and improve detection accuracy. In particular, E-Dualswin-Ynet improved by introducing SwinTransformer and Wise-IoU can more fully amplify the complementarity between infrared and visible light modalities, obviously better than other single-modal detection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： image enhancement

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Transmission Tower Detection Algorithm Based on Feature-Enhanced Convolutional Network in remote sensing image 5th

Transmission Tower Detection Algorithm Based on Feature-Enha...

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5th Chinese Conference on pattern recognition and Computer Vision (PRCV)

作者： Zhang, Zhengpeng Xie, Xinyu Song, Chenggen Dai, Dong Bu, Lijing Xiangtan Univ Coll Automat & Elect Informat Xiangtan Peoples R China Piesat Informat Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9783031189128;9783031189135

Transmission tower object detection from aerial image is an important task of power transmission fundamental infrastructure monitoring. However, the geometric variations, large-scale variation, complex background features and truss structure of transmission tower in aerial remote sensing images bring huge challenges to object detection. In this work, we propose a feature enhanced convolution network method (DSA-YOLOv3) to improve the performance of transmission tower detection in aerial images by modifying YOLOv3. The four layer feature extraction and fusion in darknet53 deep residual network is designed to enhance the feature expression ability of the network. Secondly, the DSA enhanced feature extraction module (the module consists of deformable convolution, SPP module, and attention module, which is named DSA module) is proposed. We use deformable convolution to improve the network's generalization ability due to the geometric variations of transmission tower in aerial image. Considering the large-scale variation of the transmission towers, we use the SPP module to increase the effective receptive field of the network. Then an attention module is used to eliminate the interference for the mixed characteristics of target and background environment, which is caused by truss structure of the transmission tower. Finally, we use CIoU as the loss function and DIoU as the non-maximum suppression judgment condition to improve the discrimination of highly overlapping targets. The proposed method was used in the transmission tower data sets to experiment. Experimental results demonstrate that the proposed method's AP, recall rate, and precision are 93.52%, 87.55%, and 96.16%, respectively. Compared with the original YOLOv3, the indexes of our method improved by 3.72%, 2.32%, and 5.91%, respectively.

关键词： remote sensing image Feature fusion Deformable convolution Attention mechanism Transmission tower detection

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Spatial change recognition model using artificial intelligence to remote sensing

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Procedia Computer Science 2023年 217卷 62-71页

作者： Majid Mirbod Baback Rezaei Mehrnoosh Najafi Department of Industrial Management Tehran North Branch Islamic Azad University Tehran Iran Department of Management and economics science and research Branch Islamic Azad University Tehran Iran

After the satellite's advent, whose jobs were to image the surface of the earth, big data of imaging data of the land surface was made available to researchers in various sciences to exploit in their field of work. So, remote sensing gradually came to the attention of researchers in different sciences, for example, geography, environmental science, civil engineering, etc. Each analyzed the visual data of the earth's surface from the perspective of their field. So, spatial change recognition issues and calculating the percentage of changes at the ground level has considered. The model presented is based on machine vision, image processing, and a fuzzy interface system to reveal features. This research is in the category of applied research, and, finally, an application will be present that can lead to the development of software such as Google Earth and can add to that as an option. Another of the advantages of this model is its easy use compared to specialized software such as Arc GIS. It is the novelty of this research.

关键词： Artificial intelligence Spatial change recognition remote sensing image processing remote sensing application

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A Dual Attention Fusion Network for SAR-Optical Land Use Classification Based on Semantic Balance 7

A Dual Attention Fusion Network for SAR-Optical Land Use Cla...

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7th International Conference on pattern recognition and Artificial Intelligence, PRAI 2024

作者： Yu, Ke Wang, Feng Shanghai200433 China Yiwu Research Institute Fudan University Yiwu322000 China

ISBN: (纸本)9798350350890

The rapid development of remote sensing technology has enhanced the acquisition of multi-modal data. Optical and synthetic aperture radar (SAR) images, as a uniquely complementary pair, have been proven highly effective in land use classification tasks, allowing for more accurate and detailed analysis of surface characteristics. However, existing approaches often rely on simple fusion methods and ignore the semantic relationships between modalities. Moreover, network models often focus on merging extensive contextual semantic information across channels to capture broader scene features, neglecting the connection between spatial textures and details. To address these issues, the dual attention optical and SAR image fusion network (DAOSNet) is proposed in this paper to balance semantic interactions between modalities and supplement local spatial information. DAOSN et consists of two key modules. Firstly, the dual attention fusion Transformer (DAFT) module can create cross-modal sensory fields to enhance global interactions between pixels in the spatial dimension. Additionally, the modal semantic balance (MSB) loss is introduced to promote deeper semantic collaboration between modalities and preserve the unique perspectives of each modality. The experimental results on the WHU-OPT -SAR dataset demonstrate that the proposed approach outperforms other fusion-based approaches, with an improvement of 1.4% in overall accuracy (OA), 10.4% in mean intersection over union (MIoU), and 1.8% in Kappa compared to the MCANet. © 2024 IEEE.

关键词： image fusion

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SignHRNet: Street-level traffic signs recognition with an attentive semi-anchoring guided high-resolution network

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND remote sensing 2022年第0期192卷 142-160页

作者： Yu, Yongtao Jiang, Tao Li, Yinyin Guan, Haiyan Li, Dilong Chen, Lianghai Yu, Changhui Gao, Li Gao, Shangbing Li, Jonathan Huaiyin Inst Technol Fac Comp & Software Engn Huaian 223003 Peoples R China Nanjing Univ Informat Sci & Technol Sch Remote Sensing & Geomatics Engn Nanjing 210044 Peoples R China Huaqiao Univ Coll Comp Sci & Technol Xiamen 361021 Peoples R China Univ Waterloo Dept Geog & Environm Management Waterloo ON N2L 3G1 Canada

Traffic signs are indispensable fixtures in modern transportation activities, which are installed along the roadside or over the pathway to provide important prompt messages. The timely emergences, complete visibilities, and definite signals of traffic signs matter significantly to direct the driving behaviors and ensure the convenience and security of the transportation activities. Moreover, the efficient and accurate recognition of traffic signs can also provide decisive evidences to a variety of intelligent transportation systems. Nevertheless, the unignorable presence issues of traffic signs caused by vision factors, such as small sizes, dimness, deformations, and occlu-sions, impact essentially on the high-quality recognition of traffic signs. This paper develops a novel attentive semi-anchoring guided high-resolution network, named SignHRNet, for street-level traffic signs recognition purpose. First, stacked with a high-resolution network boosted by a dual-attention module as the feature extractor, the SignHRNet can exploit informative channel features and task-oriented spatial features to generate multiscale strong feature semantics for instance-level predictions. Second, designed with a semi-anchoring guided detection strategy assisted by instance-aware feature alignment, the SignHRNet can achieve highly -efficient sign type categorization and highly-accurate sign location determination. The proposed SignHRNet is intensively evaluated on three large-size datasets towards traffic signs recognition. Quantitative verifications demonstrate a competitive accuracy with an average mAP, mAP50, and mAP75 of 72.85%, 96.48%, and 85.31%, respectively, in processing traffic signs of varying self-conditions under diverse scenarios. Ablative and comparative analyses also confirmed the practical reliability and performance superiority of the SignHRNet in traffic signs recognition applications.

关键词： Traffic signs recognition High -resolution network Feature attention Semi -anchoring Feature alignment Street -level image

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CLOUD DETECTION ON SATELLITE imageRY USING U-NET ARCHITECTURE 28

CLOUD DETECTION ON SATELLITE IMAGERY USING U-NET ARCHITECTUR...

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remote sensing of Clouds and the Atmosphere XXVIII 2023

作者： Vandoit, Alexis Blanco, Benoit Turon, Léo Killisly, Clément TotalEnergies SE France CVAssociés France France

ISBN: (纸本)9781510666894

Cloud detection holds significant importance when analyzing satellite imagery, notably in the visible domain. A wide variety of detection tools already exists for this type of application, using radiometric information. With the aim of enhancing cloud detection, recent advances in deep learning have made it possible to create tools based on pattern recognition while leveraging radiometric data. This paper aims to present a method focused on machine learning, giving details of its construction process, from the creation of the datasets to overall performance. A sample use case is also presented to demonstrate the promising outcomes obtained. Using over 600,000km2 of ground-labeled satellite imagery and a U-Net based architecture for our machine learning algorithm, we achieved encouraging performances over various land types. Our results showed significant results when evaluated on the three most frequently used metrics for image Segmentation. Our product gives interesting results in certain areas that present challenging ground types, such as snowy tops, when using Infrared imagery. © 2023 SPIE.

关键词： remote sensing

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Deep Convolutional Sparse Coding Networks for Interpretable image Fusion

Deep Convolutional Sparse Coding Networks for Interpretable ...

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2023 IEEE/CVF Conference on Computer Vision and pattern recognition Workshops, CVPRW 2023

作者： Zhao, Zixiang Zhang, Jiangshe Bai, Haowen Wang, Yicheng Cui, Yukun Deng, Lilun Sun, Kai Zhang, Chunxia Liu, Junmin Xu, Shuang Xi'an Jiaotong University China Eth Zurich Computer Vision Lab Switzerland Research and Development Institute Northwestern Polytechnical University in Shenzhen China Northwestern Polytechnical University China The University of Melbourne Australia

ISBN: (纸本)9798350302493

image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents CSCFuse, which contains three deep convolutional sparse coding (CSC) networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, multi-exposure image fusion, and multi-spectral image fusion). The CSC model and the iterative shrinkage and thresholding algorithm are generalized into dictionary convolution units. As a result, all hyper-parameters are learned from data. Our extensive experiments and comprehensive comparisons reveal the superiority of CSCF use with regard to quantitative evaluation and visual inspection. © 2023 IEEE.

关键词： image fusion

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Multi-task Learning Based Single Temporal remote sensing image Change Detection 12

Multi-task Learning Based Single Temporal Remote Sensing Ima...

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

作者： Yang, Peixuan Cao, Yichao Lu, Xiaobo College of Software Engineering Southeast University JiangSu NanJing China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education JiangSu NanJing China School of Automation Southeast University JiangSu NanJing China

ISBN: (纸本)9798400707988

Change Detection is a classic and fundamental task in remote sensing(RS), and has accomplish remarkable achievement on bitemporal image based methods. However, bitemporal images are very expensive and needs professional researchers to align and label the paired images. Furthermore, most bitemporal image based methods are only semantic-aware not instance-aware, which indicates these methods only use paired images' semantic information. Thus, we propose a method that only needs unpaired RS images with semantic labels. In this paper, we introduce multi-task learning into change detection progress, which comprises four major parts: Boundary-Aware Module, Contextual-Aware Module, Segmentation Module, Change Detection Module. At first, we construct Boundary-Aware Module and Contextual-Aware Module to extract sharp boundary and dense contextual features. Then we feed the feature maps to Change Detection Module and Segmentation Module to accomplish the subtasks. The comprehensive experiments on a single temporal image dataset demonstrate the efficiency of our approach over other bitemporal image based methods, and the experimental results shows our approach is competitive with other methods while all methods was trained on bitemporal image dataset. © 2023 ACM.

关键词： Change detection

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Spectral-spatial classification of hyperspectral images based on non-linear principal component analysis and deep learning models

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INTERNATIONAL JOURNAL OF remote sensing 2022年第17期43卷 6561-6580页

作者： Akbari, Davood Rokni, Komeil Univ Zabol Fac Engn Dept Geomat Engn Zabol Iran Gonbad Kavous Univ Fac Engn Dept Geomat Engn Gonbad E Kavus Iran

Hyperspectral sensors, due to the acquisition of a large number of spectral bands, always have particular importance in monitoring the phenomena of the earth's surface. The classification of hyperspectral images is the most crucial method of processing hyperspectral data, and many efforts have been made to increase its accuracy. In recent years, convolutional neural networks (CNNs) and spatial features have been a big part of how well hyperspectral images can be classified. CNNs, due to the automatic generation of features and reduction of parameters, compared to multilayer perceptron neural networks by sharing parameters in each layer, have received much attention from researchers in the field of pattern recognition. In previous research, much attention has not been paid to the simultaneous use of spatial feature extraction methods in CNNs. For this reason, in this paper, a new CNNs architecture has been introduced to classify hyperspectral images, which uses the spectral-spatial vector obtained from the morphological profiles method as the network's input. Morphological profiles, which include a set of opening and closing filters, are generally applied to the components of the image that contain the most information. In this research, non-linear principal component analysis (non-linear PCA) implemented by neural networks was used to summarize hyperspectral image information. Therefore, the main innovation of this paper is to provide a three-stage framework for applying deep learning. The first step is to reduce the dimensions of the hyperspectral image using non-linear PCA, the second stage is to extract spatial features using the morphological profiles method, and the third step is to prepare the inputs and design the CNNs architecture. The proposed method was evaluated on three benchmark hyperspectral images of Pavia, Berlin and Telops. The results of the experiments show the superiority of the proposed method by 13, 11 and 17% in the overall accuracy parameter

关键词： Hyperspectral image Deep learning Spectral-spatial classification Non-linear PCA Morphological profile

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image Classifications Methods Analysis with Differen Methods to for Identifying best image Layout with High Resolution

Image Classifications Methods Analysis with Differen Methods...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Mahesh, T.R. Vivek, V. Faculty of Engineering and Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350322309

This researcher investigated effective much eye opening and pattern detection algorithms. Finally, this article used two frameworks to argue that geospatial investigation systems for patterns are necessary. One of most popular method for analyzing photographs using image processing and analysis math is probably visual configuration methods. imagery description is also necessary for the evaluation of remote sensing tools used for thorough land investigation. The three main image configuration techniques that are often used for far detection are opinion piece visual analysis, controlled groupings, and solos visual categorization. The approach used in these image layout techniques is combined in the present study. The present study also briefly summarizes each approach's use in exploring distant sensing info while also outlining the steps for each way using stream outlines and frameworks. Generally speaking, a quick overview of image layout techniques is included in the test. © 2023 IEEE.

关键词： image classification

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