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Intelligent recognition of Point Source Target image Control Points with Simulation Datasets 1

6th Asian Conference on pattern recognition (ACPR)

作者： Li, Kai Yang, Weiming Zhang, Lei Zhang, Zhenchao Acad Syst Engn Acad Mil Sci Beijing Peoples R China Strateg Support Force Informat Engn Univ Sch Geospatial Informat Zhengzhou Peoples R China

ISBN: (数字)9783031024443

ISBN: (纸本)9783031024443;9783031024436

The point source target (PST) can provide high object and image positioning accuracy and is expected to play an important role in the precise geometric processing of optical remote sensing sensors in the future. This paper proposes a method for intelligently recognizing PST image control points (ICPs) from satellite imagery, which can improve the intelligent level of geometric processing of optical remote sensing sensors. Two deep convolutional neural networks (DCNNs), Faster R-CNN and CenterNet are selected to complete the recognition task. Due to the lack of training data, a large number of simulated samples are generated considering the PST image characteristics. The simulated and real PST ICPs are then used to test the trained DCNNs. The two DCNNs complete the recognition task on the simulated dataset successfully. The Recall and Precision values of the two DCNNs are close to 100%. The performance of the two DCNNs on real PST ICPs is worse than that on the simulated data, but the recognition task is also well completed when the quality of PST ICPs is good. The Recall values of both models are above 95%, and the Precision values are close to 100%. Experiment results also show that the performance of CenterNet is better than Faster R-CNN and the image quality has a great impact on the recognition performance.

关键词： Point source target (PST) image control point (ICP) Deep convolutional neural networks (DCNNs) Faster R-CNN CenterNet

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PADDY RICE recognition USING AN INTEGRATION OF SATELLITE image DATA AND AI MODELS 44

PADDY RICE RECOGNITION USING AN INTEGRATION OF SATELLITE IMA...

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44th Asian Conference on remote sensing, ACRS 2023

作者： Hsu, Yi-Yun Chang, Li-Yu Satellite Data Processing Division Taiwan Space Agency 8F 9 Prosperity 1st Road Hsinchu Science Park HsinChu City300 Taiwan

ISBN: (纸本)9781713893646

Since the temporal variation for growing period of paddy rice can be shown clearly in optical and radar images, a Long-Short Term Memory (LSTM) model is introduced to construct paddy rice recognition systems using Sentinel-1 and FORMOSAT-2 time-series data. Firstly, we investigate a one-stage paddy rice recognition system by using multi-sensor satellite images, respectively. Then a two-stage paddy rice classification is proposed to overcome the drawback in previous study. We also investigate the inconsistencies of ground truth and remote sensing data. In this article, several cases of study would also be compared. © 2023 ACRS. All Rights Reserved.

关键词： Artificial Intelligent Deep Learning Geomatic image recognition

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Research on Blind Restoration Algorithm of Motion Blurred remote sensing image 10th

Research on Blind Restoration Algorithm of Motion Blurred Re...

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10th International Conference on Signal and Information processing, Network and Computers, ICSINC 2022

作者： Yang, Hanwen Liu, Yanran Luan, Li Wang, Yunsen Wang, Haoxuan Shanghai Jiao Tong University Shanghai200240 China Shandong Institute of Space Electronic Technology Shandong Yantai264670 China

ISBN: (纸本)9789811999673

The reconstruction of motion blur is a significant subject in remote sensing image processing. It has a great effect on the follow-up processes of target detection and recognition. To meet the needs of on-board intelligent processing, a new blind restoration method based on Radon transform and autocorrelation function is proposed to process remote sensing image blurred by motion. The method adopt frequency spectrum diagram and Radon transform to calculate the direction of motion blur image. Autocorrelation function is quoted to calculate the blur scale. After the point spread function is estimated, Wiener filter is applied to realize the restoration of motion blurred images. By rotating blurred image to the horizontal axis direction, the two-dimensional restoration problem is converted into one dimension. Test results indicate that the method can obtain satisfactory recovering results of remote sensing image blurred by linear motion. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： image reconstruction

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Enhancing Text Extraction from Natural Scene images Through Diffusion Models

Enhancing Text Extraction from Natural Scene Images Through ...

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

作者： Jia, Baojun Zhang, Wenyu Chen, Yifan Ma, Jin University of Science and Technology of China Hefei Hefei China China Merchants Bank Chengdu Chengdu China

ISBN: (纸本)9781510680012

This paper focuses on super-resolution of natural scene text images, serving as a crucial preprocessing step for image recognition, with the aim of extracting text information from low-resolution images. In this preprocessing step, the primary challenges stem from the impact of factors such as lighting, occlusion, and distortion on text features in natural scenes, as well as the tendency of small pixel values in low-resolution images to be overlooked by large models. Previous works have primarily focused on the capability to extract features. However, the key to addressing this problem lies in restoring the different distributions between high-resolution and low-resolution images. As widely acknowledged, diffusion models excel in learning to generate distributions from datasets. Therefore, we introduce a conditional diffusion model to learn feature distributions among diverse images and apply histogram matching to address the issue of n patches. Our experimental results confirm the effectiveness of our approach. © 2024 SPIE.

关键词： image recognition

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Cross-dimensional feature attention aggregation network for cloud and snow recognition of high satellite images

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NEURAL COMPUTING & APPLICATIONS 2024年第14期36卷 7779-7798页

作者： Hu, Kai Zhang, Enwei Xia, Min Wang, Huiqin Ye, Xiaoling Lin, Haifeng Nanjing Univ Informat Sci & Technol Jiangsu Collaborat Innovat Ctr Atmospher Environm Nanjing 210044 Peoples R China Nanjing Forestry Univ Coll Informat Sci & Technol Nanjing 210037 Peoples R China

Cloud and snow in remote sensing images typically block the underlying surface information and interfere with the extraction of available information, so detecting cloud and snow becomes a critical problem in remotely sensed image processing. The current methods for detecting clouds and snow are susceptible to interference from complex background, making it difficult to recover cloud edge details and causing missing and false detection phenomena. To address these issues, a cross-dimensional feature attention aggregation network is suggested to realize the segmentation of clouds and snow. To address the problem of interference induced by the similar spectral characteristics of clouds and snow, the context attention aggregation module is added to conflate feature maps of various dimensions and screen the information. Multi-scale strip convolution module (MSSCM) and its improved version MSSCMs are used to extract edge characteristics at different scales and improve the harsh segmentation border. Also, adding deep feature semantic information extraction module to deep features to guide the classification of the model to avoid the interference of complex background. Finally, a 'los beatles' module is used to replace the traditional linear combination in the decoding stage, and the feature information of different granularity is fused and extracted to enhance the model's detection efficiency. In this paper, experiments are carried out on the public datasets: CSWV, HRC_\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\_$$\end{document}WHU and L8_\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\_$$\end{document}SPARCS

关键词： Cloud and snow identification remote sensing image Cross-dimensional feature attention Deep learning

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Aircraft Type recognition in remote sensing images using Mean Interval Kernel

Aircraft Type Recognition in Remote Sensing Images using Mea...

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International Conference on image processing and Vision Engineering (IMPROVE)

作者： Sharma, Jaya Datla, Rajeshreddy Sravani, Yenduri Chalavadi, Vishnu Mohan, Krishna C. Indian Inst Technol Hyderabad Dept Comp Sci & Engn Hyderabad India Adv Data Proc Res Inst ADRIN Dept Space Secunderabad India

ISBN: (纸本)9789897585630

Structural characteristics representation and their fine variations are crucial for the recognition of different types of aircrafts in remote sensing images. Aircraft type classification across different sensor remote sensing images by spectral and spatial resolutions of objects in an image involves variable length spatial pattern identification. In our proposed approach, we explore dynamic kernels to deal with variable length spatial patterns of aircrafts in remote sensing images. A Gaussian mixture model (GMM), namely, structure model (SM) is trained over aircraft scenes to implicitly learn the local structures using the spatial scale-invariant feature transform (SIFT) features. The statistics of SM are used to design dynamic kernel, namely, mean interval kernel (MIK) to deal with the spatial changes globally in the identical scene and preserve the similarities in local spatial structures. The efficacy of the proposed method is demonstrated on the multi-type aircraft remote sensing images (MTARSI) benchmark dataset (20 distinct kinds of aircraft) using MIK. Also, we compare the performance of the proposed approach with other dynamic kernels, such as supervector kernel (SVK) and intermediate matching kernel (IMK).

关键词： remote sensing images Aircraft Type recognition Structural Information Model Scale-invariant Feature Transform (SIFT) Dynamic Kernels

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Feature Aggregation Network for Building Extraction from High-Resolution remote sensing images 20th

Feature Aggregation Network for Building Extraction from H...

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20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Zhou, Xuan Wei, Xuefeng Institut Polytechnique de Paris Rte de Saclay Palaiseau91120 France

ISBN: (纸本)9789819970247

The rapid advancement in high-resolution satellite remote sensing data acquisition, particularly those achieving sub-meter precision, has uncovered the potential for detailed extraction of surface architectural features. However, the diversity and complexity of surface distributions frequently lead to current methods focusing exclusively on localized information of surface features. This often results in significant intra-class variability in boundary recognition and between buildings. Therefore, the task of fine-grained extraction of surface features from high-resolution satellite imagery has emerged as a critical challenge in remote sensing image processing. In this work, we propose the Feature Aggregation Network (FANet), concentrating on extracting both global and local features, thereby enabling the refined extraction of landmark buildings from high-resolution satellite remote sensing imagery. The Pyramid Vision Transformer captures these global features, which are subsequently refined by the Feature Aggregation Module and merged into a cohesive representation by the Difference Elimination Module. In addition, to ensure a comprehensive feature map, we have incorporated the Receptive Field Block and Dual Attention Module, expanding the receptive field and intensifying attention across spatial and channel dimensions. Extensive experiments on multiple datasets have validated the outstanding capability of FANet in extracting features from high-resolution satellite images. This signifies a major breakthrough in the field of remote sensing image processing. We will release our code soon. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Extraction

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Enhanced Multi-Scale Ship Detection Through Joint Multi-Source image Training 12

Enhanced Multi-Scale Ship Detection Through Joint Multi-Sour...

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12th IEEE International Conference on Information and Communication Networks, ICICN 2024

作者： Huang, Yuan Cao, Minghua Zhang, Yue Ning, Cheng School of Computer and Communication Lanzhou University of Technology Lanzhou China

ISBN: (纸本)9798350355802

In order to improve the detection performance of traditional single-source remote sensing image ship detection methods in terms of anti-interference capability and multi-scale targets. We develop an improved algorithm based on YOLOv8, called Sea-YOLOv8, and obtain the multi-source remote sensing image detection model SAR-YOLOv8 through migration ***, Efficient Multi-Scale Convolution (EMSConv) is used to improve the ability of the C2f module in capturing the features of ships at different scales by using different sizes of convolution kernels. scale ship features using different sized convolution kernels. Secondly, Large Separable Kernel Attentio (LSKAtt) is applied to the SPPF module to decompose the 2D convolution kernel into horizontal and vertical 1D kernels, which improves the computational efficiency while maintaining the high efficiency image processing capability. Experimental results on the SeaShips and SSDD datasets show that Sea-YOLOv8 has a good performance in terms of R, mAP, model parameters and FPS have achieved satisfactory results. Finally, the optimal transfer learning strategy was determined through layer-by-layer freezing, training and validation, which led to the construction of the multi-source remote sensing image recognition model SAR-YOLOv8. © 2024 IEEE.

关键词： remote sensing

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DCA-Unet: Enhancing small object segmentation in hyperspectral images with Dual Channel Attention Unet

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JOURNAL OF THE FRANKLIN INSTITUTE 2025年第4期362卷

作者： Han, Kunbo Chen, Mingjin Gao, Chongzhi Qing, Chunmei Guangzhou Univ Sch Comp Sci & Cyber Engn Guangzhou 510006 Peoples R China South China Univ Technol Sch Elect & Informat Engn Guangzhou 510641 Guangdong Peoples R China

Hyperspectral image (HSI), with its high spectral resolution, captures extensive information across multiple wavelengths beyond the visible spectrum, enabling the recognition of intricate object details and features. This capability renders HSI indispensable in scientific research and engineering applications. Despite the efficacy of fully convolutional networks in processing remote sensing data, current methods face challenges in accurately segmenting small objects in HSI and delineating the boundaries of similar or adjacent objects. To address these limitations, we propose a novel DCA-Unet framework for HSI semantic segmentation. This framework leverages a dual-channel attention module to capture feature dependencies across both spatial and spectral channel dimensions, thereby enriching contextual information. Specifically, positional and channel attention modules are incorporated after each layer of the Unet encoder to enhance pixel-level representation and spectral inter-channel dependencies, respectively. The fused output of these attention modules further strengthens the feature representation of the Unet encoder. In the final output, Dice loss is employed to quantify the overlap between predicted and actual segmentations, while Focal loss is utilized to balance background samples, thus improving segmentation performance for small objects. Experimental results demonstrate that the proposed DCA-Unet framework excels in HSI semantic segmentation tasks, particularly in the accurate segmentation of small objects.

关键词： Hyperspectral image Semantic segmentation Dual channel attention module

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A Novel Lightweight CNN for Constrained IoT Devices: Achieving High Accuracy With Parameter Efficiency on the MSTAR Dataset

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IEEE ACCESS 2024年 12卷 160284-160298页

作者： Rahman, Noor Khan, Muzammil Ullah, Israr Kim, Do-Hyeun Virtual Univ Pakistan Dept Comp Sci & Informat Technol Peshawar Campus Lahore 44090 Pakistan Univ Swat Dept Comp & Software Technol Swat 53179 Khyber Pakhtunk Pakistan Jeju Natl Univ Dept Comp Engn Jeju 63243 South Korea

Convolutional neural networks (CNNs) have revolutionized fields such as image classification, natural language processing (NLP), and object detection. remote sensing is no exception, particularly synthetic aperture radar (SAR) image analysis, where it addresses the challenge of classifying inherently noisy and indistinct images. Traditionally, this task relied heavily on manual intervention, making it time-intensive. With automation becoming increasingly critical, research has shifted toward deep learning techniques. However, many existing approaches are computationally intensive and require substantial memory resources. In this study, we present a lightweight CNN specifically designed for SAR applications, tested on the moving and stationary target acquisition and recognition (MSTAR) dataset. Our model surpasses previous studies by demonstrating higher accuracy while utilizing significantly fewer parameters. This novel architecture achieves an optimal balance between accuracy and computational efficiency. This is particularly useful in a resource-constrained environment in many real-world applications. More specifically, our proposed CNN model demonstrates robust performance across various scenarios, achieving an accuracy of 99.7% in classifying the three target classes under standard operating conditions (SOCs). Furthermore, when extended to classify ten classes, our proposed model outperforms several baseline algorithms from state-of-the-art literature. This research attempts to achieve a trade-off between model performance and model size, contributing to the development of CNNs suitable for resource-limited applications, particularly targeting IoT deployments. The findings present a practical solution for situations that prioritize both accuracy and resource conservation, thereby advancing discussions on efficient model design.

关键词： Feature extraction Accuracy Target recognition Convolutional neural networks Internet of Things Computational modeling Radar polarimetry Synthetic aperture radar Computer architecture Training Automatic target recognition (ATR) CNN synthetic aperture radar (SAR) image classification Internet of Things (IoT)

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