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Earth remote sensing and Geographic Information Systems

pattern recognition AND image ANALYSIS 2023年第4期33卷 1129-1141页

作者： Soifer, V. A. Sergeev, V. V. Kopenkov, V. N. Chernov, A. V. Russian Acad Sci Image Proc Syst Inst Fed Sci Res Ctr Crystallog & Photon Samara 443001 Russia Samara Univ Samara 443080 Russia

The article examines the role and place of Earth remote sensing (ERS) in geographic information systems. The stages of development of remote sensing and geoinformatics are given, as well as a brief overview of Russian means of obtaining, receiving, and processing satellite images. The specifics and tasks of processing remote sensing data, including hyperspectral data, as well as the experience of using remote sensing data and geoinformation to solve practical problems of managing the territory of the Samara oblast are considered.

关键词： remote sensing geographic information systems spacecraft image processing big geodata hyperspectral equipment

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Diffusion model for multi-scale ship object detection and recognition in remote sensing images

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SIGNAL image AND VIDEO processing 2025年第1期19卷 1-13页

作者： Chen, Lei Wang, Bin Liu, Ying Zhao, Shuang Guan, Qinghe Li, Guandian Changchun Univ Sci & Technol Coll Elect & Informat Engn Nanhu St Changchun 130022 Jilin Peoples R China

Ship object detection and recognition in remote sensing images (RSIs) is a challenging task due to the multi-scale and complex background characteristics of ship objects. Currently, convolution-based methods cannot adequately solve these problems. Firstly, this paper first applies the diffusion model to the task of ship object detection and recognition in RSIs, and proposes a new diffusion model for multi-scale ship object detection and recognition in remote sensing images (MSDiffDet). Secondly, in order to reduce the loss of multi-scale information in the feature extraction process, this paper proposes the Channel Fusion FPN (CF-FPN) based on FPN and constructs the Large-Scale Feature Enhancement Module (LSFEM), which further enhances the algorithm's ability to extract large-scale ship object features and improves the detection accuracy of ship objects in RSIs. Finally, this paper prunes and reconstructs MobileNetV2 to obtain the Sparse MobileNetV2, which is used as the backbone network of the image encoder, which enhances detection accuracy while reducing the overall parameter count of the algorithm. The experimental results demonstrate that the MSDiffDet algorithm is effective in detecting and recognizing four types of remote sensing ship objects: aircraft carriers, warships, commercial ships, and submarines. The mAP0.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$mAP_{0.5}$$\end{document} achieved a notable 89.8%. A significant improvement of 5.8% in mAP0.5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$mAP_{0.5}$$\end{document} is observed compared to the DiffusionDet algorithm, indicating the potentia

关键词： Ship object detection and recognition Diffusion model remote sensing image CF-FPN MobileNetV2

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Fast Projected Fuzzy Clustering With Anchor Guidance for Multimodal remote sensing imagery

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IEEE TRANSACTIONS ON image processing 2024年 33卷 4640-4653页

作者： Zhang, Yongshan Yan, Shuaikang Zhang, Lefei Du, Bo China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Wuhan Univ Sch Comp Sci Wuhan 430072 Peoples R China

Multimodal remote sensing image recognition is a popular research topic in the field of remote sensing. This recognition task is mostly solved by supervised learning methods that heavily rely on manually labeled data. When the labels are absent, the recognition is challenging for the large data size, complex land-cover distribution and large modality spectrum variation. In this paper, a novel unsupervised method, named fast projected fuzzy clustering with anchor guidance (FPFC), is proposed for multimodal remote sensing imagery. Specifically, according to the spatial distribution of land covers, meaningful superpixels are obtained for denoising and generating high-quality anchor. The denoised data and anchors are projected into the optimal subspace to jointly learn the shared anchor graph as well as the shared anchor membership matrix from different modalities in an adaptively weighted manner to accelerate the clustering process. Finally, the shared anchor graph and shared anchor membership matrix are combined to derive clustering labels for all pixels. An effective alternating optimization algorithm is designed to solve the proposed formulation. This is the first attempt to propose a soft clustering method for large-scale multimodal remote sensing data. Experiments show that the proposed FPFC achieves 81.34%, 55.43% and 93.34% clustering accuracies on the three datasets and outperforms the state-of-the-art methods.

关键词： Fuzzy clustering multimodal remote sensing anchor graph projection learning Fuzzy clustering multimodal remote sensing anchor graph projection learning

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Equivariant Attention Graph Capsule Network for remote sensing Scene recognition

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IEEE GEOSCIENCE AND remote sensing LETTERS 2025年 22卷

作者： Bian, Xiaoyong Chen, Xi Yu, Guorong Du, Qian Wuhan Univ Sci & Technol Sch Comp Sci Wuhan 430074 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

In order to effectively exploit foreground object structures in remote sensing scene recognition, it is crucial to hierarchically parse foreground objects and learn invariant feature representation by adding an equivariant regularization (ER) term to the graph capsule network. Traditionally such equivariance is constructed using group convolutions, which become intractable when composing complex transformations, leading to increased inference time. In addition, global average pooling (GAP) can result in the loss of useful information in the captured features. To deal with this issue, we propose an equivariant attention graph capsule network (EA-GraCaps) in this letter. EA-GraCaps can progressively learn important cues of foreground objects and model potential spatial relations among parts in a transformation equivariance fashion. Specifically, the intragroup capsule layer is first fed to the graph pooling module for preliminary voting, then the intergroup capsules are input into the dual mixing attention (MA) module to refine the votes for coincidence filtering. With this formulation, our approach can characterize spatial hierarchies between object parts and improve the discriminative ability of class capsules. Experimental results demonstrate that the proposed EA-GraCaps can yield superior classification performance on three widely used benchmarks.

关键词： image reconstruction Routing remote sensing image recognition Accuracy Training Robustness pattern recognition Autoencoders Vectors Capsule network dual mixing attention (MA) equivariant remote sensing scene recognition

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Towards constructing a DOE-based practical optical neural system for ship recognition in remote sensing images

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SIGNAL processing 2024年 221卷

作者： Li, Yanbing Li, Shaochong Li, Tao Wang, Guoqing Liu, Xun Yang, Wei Liu, Yuan'an Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 100094 Peoples R China Beijing Inst Space Mech & Elect Dept Imaging Technol Beijing 611731 Peoples R China

The optical neural network system based on the 4f system (4f-ONN) is a feasible solution for on -orbit real-time target detection and recognition on remote sensing images, as it can directly modulate and encode the twodimensional image information. However, traditional 4f systems based on spatial light modulators (SLMs) often encounter misalignment errors during assembly due to the reflective optical path used in SLMs. Additionally, implementing electronic SLM in space -based applications introduces problems such as particle number reversal, significantly reducing the reliability of the system. To address these issues, this paper proposes the adoption of diffractive optical elements (DOEs) to construct the 4f-ONN system. The DOE -based design offers a more compact structure, enhanced reliability, and reduced energy consumption, making it highly suitable for on -orbit image processing applications. Due to the expensive and time-consuming nature of the DOE manufacturing process, a design approach for a 4f system based on DOE was pursued through software simulation in this paper. The simulation phase involved the utilization of electronic neural networks to acquire the physical parameters of the DOE mask, while incorporating the array theorem and Fraunhofer diffraction theorem to accurately calculate the physical dimensions of the DOE. The effectiveness of the adopted DOE -based 4f system was initially validated through experiments on a simple pattern dataset. Subsequently, simulation experiments were conducted on three public datasets, namely Mnist, Fashion-mnist, and QuickDraw16, to confirm the efficacy of the DOE -based 4f system in classification tasks. Lastly, target recognition experiments were performed on the GF-2 dataset, and a corresponding hardware system was developed to demonstrate the potential of the DOE -based 4f system in on -orbit image processing.

关键词： Optical neural network remote sensing 4f system Diffractive optical element

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RSID: A remote sensing image Dehazing Network 6th

RSID: A Remote Sensing Image Dehazing Network

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6th Chinese Conference on pattern recognition and Computer Vision (PRCV)

作者： Li, Yuan Zhao, Yafeng Northeast Forestry Univ 26 Hexing Rd Harbin 150036 Heilongjiang Peoples R China

ISBN: (纸本)9789819985364;9789819985371

Hazy images often lead to problems such as loss of image details and dull colors, which significantly affects the information extraction of remote sensing images, so it is necessary to research image dehazing. In the field of remote sensing, remote sensing images are characterized by large-size and rich information, so the processing of remote sensing images often has the problems of GPU memory overflow and difficult removal of non-uniform haze. For remote sensing image characteristics, an efficient and lightweight end-to-end dehazing method is proposed in this paper. We use the FA attention combined with smoothed dilated convolution instead as the main structure of the encoder, which can achieve imbalanced handling of hazy images with different levels of opacity while reducing parameter count. Channel weight fusion self-attention is added in the decoder part to realize the automatic learning and pixel-level processing of different receptive field features We tested the proposed method on both public datasets RESIDE and real large-size hazy remote sensing images. The proposed method achieved satisfactory results in our experiments, which proves the effectiveness of the proposed method.

关键词： Hazy images FA attention Channel weight fusion self-attention UAV remote sensing smoothed dilated convolution

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RETRACTION: Application of motion trajectory recognition based on remote sensing image optical processing in optimizing swimming training schemes

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OPTICAL AND QUANTUM ELECTRONICS 2024年第10期56卷 1-1页

作者： Jiang, Wei Jiangsu Univ Sports Dept Zhenjiang 212013 Jiangsu Peoples R China

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Rethinking high-resolution remote sensing image segmentation not limited to technology: a review of segmentation methods and outlook on technical interpretability

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INTERNATIONAL JOURNAL OF remote sensing 2024年第11期45卷 3689-3716页

作者： Chong, Qianpeng Ni, Mengying Huang, Jianjun Wei, Guangyi Li, Ziyi Xu, Jindong Yantai Univ Sch Comp & Control Engn 32 Qingquan Rd Yantai Peoples R China

The intelligent segmentation of high-resolution remote sensing (HRS) image, also called as dense prediction task for HRS image, has been and will continue to be important research in the remote sensing community. In recent years, the growing wave of artificial intelligence (AI) technology has introduced innovative paradigms to this domain, yielding outstanding results and overcoming many challenges with conventional segmentation techniques. This paper provides a comprehensive review of these intelligent segmentation methodologies, including traditional pattern recognition, convolution neural network (CNN)-based, and Transformer-based techniques. However, the explosive but incomplete development of intelligent segmentation techniques also poses more challenges for earth observation experts, the most of which is the technical interpretability. Consequently, we consider these segmentation techniques in the aspect of explainable artificial intelligence (XAI). Data-centric XAI thinks the practical applications of the segmentation model while model-centric XAI will facilitate the understanding of decision-making processes and the adjustment of structural features. Moreover, this review identifies novel research questions and provides constructive insights and recommendations to HRS image segmentation tasks, which may shed new light on the intelligent segmentation methods within the remote sensing image understanding community.

关键词： AI earth observation remote sensing segmentation

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A Fusion Method Incorporating Dual-Attention Mechanism and Transfer Learning Into UNet plus plus for remote sensing image Coastline Extraction

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IEEE ACCESS 2025年 13卷 11320-11331页

作者： Song, Yanru Xue, Bai Meng, Yueyue Qin, Xiang Li, Yixiao Liu, Qi China Aero Geophys Survey & Remote Sensing Ctr Nat Beijing 100083 Peoples R China MNR Land Satellite Remote Sensing Applicat Ctr Beijing 100048 Peoples R China

The segmentation of land and sea in remote sensing imagery is of great significance for coastline extraction and dynamic monitoring. Traditional coastline recognition and extraction methods based on spectral features and image processing can only generate limited image feature results when facing the complex textures and spatial distributions of high-spatial resolution remote sensing images, leading to low accuracy in segmentation outcomes. This paper applies a deep convolutional neural network to the problem of sea-land segmentation in high-spatial resolution remote sensing images and innovates upon the classic encoder-decoder architecture. Firstly, to enhance the network's ability to distinguish coastlines, a dual attention mechanism is introduced into the UNet++ network to improve the learning capacity of coastline features while suppressing the learning of non-coastline features. Secondly, an improved joint loss function is adopted to enhance training effectiveness, thereby significantly improving the accuracy of semantic segmentation. Lastly, transfer learning is utilized to strengthen the detailed features of coastlines and enhance the network's ability to identify them. Experimental results using the GID dataset for coastline segmentation demonstrate that compared to the latest algorithms such as PSPNet, CS-Deeplab v3+, and UNet++, the improved UNet++ network achieves lower boundary blurriness and more accurate segmentation results for coastlines, with fewer missed and false detections. Amidst the proliferation of high-spatial resolution remote sensing image data, the utilization of the enhanced UNet++ model for coastline extraction has demonstrated remarkable abilities in preserving boundary information and achieving superior semantic segmentation performance. This advancement enables a more refined extraction of spatial distributions, textures, and spectral features from these images, ultimately contributing to an improvement in classification accuracy.

关键词： Coastlines remote sensing Feature extraction Sea measurements Attention mechanisms Accuracy Spatial resolution Coastline extraction high-spatial resolution remote sensing imagery UNet plus plus dual attention joint loss function transfer learning transfer learning

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Unveiling decision-making in remote sensing with deep learning interpretability 12

Unveiling decision-making in remote sensing with deep learni...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Tian, Zejie School of Information and Communication Engineering Communication University of China Beijing China

ISBN: (数字)9781510674967

ISBN: (纸本)9781510674950

Deep neural networks have emerged as the predominant technical approach for remote sensing image interpretation and processing, surpassing traditional methods in various tasks such as target extraction, classification, and recognition. However, the decision-making processes underlying these deep networks usually lack transparency, making interpretability a pressing concern. In response to this concern, we employ four prominent feature attribution methods, namely Integrated Gradients, GradientShap, Occlusion, and Saliency, to perform interpretability analysis on deep learning models designed for visible light remote sensing image classification. Our objective is to unveil the foundational principles guiding the decision-making in remote sensing image classification and recognition. We also assess the effectiveness of these attribution methods in identifying crucial decision regions. Through our visual attribution analysis, we aim to contribute to a better understanding of the decision-making mechanisms employed by remote sensing image classification models. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： remote sensing

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