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3rd International conference on Computer Vision and pattern Analysis, ICCPA 2023

作者： Zhang, Xuebo Jiang, Min Li, Jinli School of Space Information Space Engineering University Beijing China 96944 troops Chinese People's Liberation Army Beijing China

ISBN: (纸本)9781510667563

Pixel-level sea-land segmentation on high-resolution remote sensing images is a basic task in remote sensing applications and is of great significance for coastline extraction and near-shore marine target detection. This paper proposes an improved Unet model for sea-land segmentation on high-resolution remote sensing images. This model outperforms UNet++ and other models in sea-land segmentation accuracy when applied to the HRSC2016-SL dataset. Based on this model, a parallel sea-land segmentation processing algorithm was developed, whose parallel efficiency reached 49.5% on 16K*14K remote sensing images. To complete this study, a parallel processing system for sea-land segmentation was developed, which achieved distributed and single-machine multi-core parallel sea-land segmentation tasks on high-resolution remote sensing images. © 2023 SPIE.

关键词： image segmentation

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Automatic detection and recognition of humans with YOLO-based deep learning using MuSES-generated EO/IR synthetic imagery 2

Automatic detection and recognition of humans with YOLO-base...

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conference on Synthetic Data for Artificial Intelligence and Machine Learning - Tools, Techniques, and Applications II

作者： Klein, Mark D. Edel, Zachary J. Packard, Corey D. Hendrickson, Jacob N. Levanen, Audrey Rynes, Peter L. ThermoAnalytics Inc 23440 Airpk Blvd Calumet MI 49913 USA

ISBN: (纸本)9781510673892;9781510673885

Machine learning algorithms are capable of processing image-based scenes, detecting and recognizing embedded targets. This has been demonstrated by data scientists and computer vision engineers, but performant algorithms must be robustly trained to successfully complete such a complex task. This typically requires a large set of training data on which the algorithm can base statistical predictions. Electro-optical infrared (EO/IR) remote sensing applications necessitate a substantial image database with suitable variation for adept learning to occur. For human detection/recognition applications diversity in clothing ensembles, pose, season, times of day, sensor platform perspectives, scene backgrounds and weather conditions can be included in training image sets to ensure sufficient input variety. However, acquiring such a diverse image set from measured sources can be a challenge, especially in thermal infrared wavebands (e.g., MWIR and LWIR). Alternatively, generating synthetic imagery with appropriate features is possible and has been shown to perform well, but a careful methodology must be followed if robust training is to be accomplished. In this work, MuSES and CoTherm are used to generate synthetic EO/IR remote sensing imagery of various human dismounts with a range of clothing, poses and environmental factors. The performance of a YOLO ("you only look once") deep learning algorithm is studied, and sensitivity conclusions are discussed.

关键词： EO/IR scene simulation machine learning artificial intelligence target signature automated image generation signature prediction deep learning EO/IR signature prediction

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Intensity-Sensitive Similarity Indexes for image Quality Assessment 26

Intensity-Sensitive Similarity Indexes for Image Quality Ass...

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26th International conference on pattern recognition / 8th International Workshop on image Mining - Theory and Applications (IMTA)

作者： Li, Xiaotong Armour, Wesley Univ Oxford Dept Engn Sci Oxford E Res Ctr Oxford England

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

The importance of image quality assessment (IQA) is ever increasing due to the fast paced advances in imaging technology and computer vision. Among the numerous IQA methods, Structural SIMilarity (SSIM) index and its variants are better matched to the perceived quality of the human visual system. However, SSIM methods are insufficiently sensitive, when images contain low information, where the important information only occupies a low proportion of the image while most of the image is noise-like, which is common in scientific data. Therefore, we propose two new IQA methods, InTensityWeighted SSIM index and Low-Information Similarity Index, for such low information images. In addition, auxiliary indexes are proposed to assist with the assessment. The application of these new IQA methods to natural images and field-specific images, such as radio astronomical images, medical images, and remote sensing images, are also demonstrated. The results show that our IQA methods perform better than state-of-the-art SSIM methods for differences in high-intensity parts of the input images and have similar performance to that of the original and gradient-based SSIM for differences in low-intensity parts. Different similarity indexes are suitable for different applications, which we demonstrate in our results.

关键词： image quality Computer vision Sensitivity Visual systems pattern recognition Indexes remote sensing

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Hyperspectral and LiDAR Data Classification Based on Structural Optimization Transmission

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第5期53卷 3153-3164页

作者： Zhang, Mengmeng Li, Wei Zhang, Yuxiang Tao, Ran Du, Qian Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Beijing Key Lab Fract Signals & Syst Beijing 100081 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

With the development of the sensor technology, complementary data of different sources can be easily obtained for various applications. Despite the availability of adequate multisource observation data, for example, hyperspectral image (HSI) and light detection and ranging (LiDAR) data, existing methods may lack effective processing on structural information transmission and physical properties alignment, weakening the complementary ability of multiple sources in the collaborative classification task. The complementary information collaboration manner and the redundancy exclusion operator need to be redesigned for strengthening the semantic relatedness of multisources. As a remedy, we propose a structural optimization transmission framework, namely, structural optimization transmission network (SOT-Net), for collaborative land-cover classification of HSI and LiDAR data. Specifically, the SOT-Net is developed with three key modules: 1) cross-attention module;2) dual-modes propagation module;and 3) dynamic structure optimization module. Based on above designs, SOT-Net can take full advantage of the reflectance-specific information of HSI and the detailed edge (structure) representations of multisource data. The inferred transmission plan, which integrates a self-alignment regularizer into the classification task, enhances the robustness of the feature extraction and classification process. Experiments show consistent outperformance of SOT-Net over baselines across three benchmark remote sensing datasets, and the results also demonstrate that the proposed framework can yield satisfying classification result even with small-size training samples.

关键词： Laser radar Feature extraction Optimization Indexes Hyperspectral imaging Collaboration Task analysis Collaborative classification convolutional neural network (CNN) deep learning hyperspectral image (HSI) light detection and ranging (LiDAR) data pattern recognition remote sensing

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

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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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Satellite image Classification Using Convolutional Neural Networks 1

Satellite Image Classification Using Convolutional Neural Ne...

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1st IEEE International conference on Smart and Sustainable Developments in Electrical Engineering, SSDEE 2025

作者： Bhaskar, Manoj Barde, Chetan Ranjan, Prakash Kumar, Nawneet Kumar, Sujit Indian Institute of Information Technology Bhagalpur Dept. of Electronics and Communication Engineering Bihar Bhagalpur India Government Engineering College Banka Bihar Engineering University Dept. of Electrical Engineering Science Technology and Technical Education Department Bihar India

ISBN: (纸本)9798331542108

image classification is crucial in remote sensing, image analysis, and pattern recognition. Object detection and classification in satellite images are crucial for applications like marine monitoring, land-use planning, ecological research, and military operations. Satellite images provide valuable spatial and temporal data, helping address real-world challenges. However, issues like data quality, availability, and distribution complicate image analysis. Deep learning (DL) algorithms have become popular for their effectiveness in classifying satellite images, sup-porting tasks such as disaster management, land-use planning, and natural resource management. This study proposed a CNN model with batch normalization for satellite image classification. The dataset includes images categorized into cloudy regions, deserts, water bodies, and green spaces. CNN excels at extracting features like edges and textures, while Batch Normalization (BN) stabilizes training by normalizing inputs and reducing covariate shifts. Together, CNN and BN improve the efficiency and accuracy of the model. The proposed model successfully classifies satellite images, outperforming existing methods with an overall accuracy of 91%, demonstrating its superior remote sensing imaae analysis capability. © 2025 IEEE.

关键词： Water management

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Multi-Plant Leaf Disease Detection and Classification using Convolution Neural Network Aad image processing

Multi-Plant Leaf Disease Detection and Classification using ...

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2024 IEEE International conference on Recent Advances in Science and Engineering Technology, ICRASET 2024

作者： Aishwarya, S.S. Ravikumar, Pushpa Chaithra, I.V. Sagar, Deeksha AIT Adichunchanagiri Institute of Technology Karnataka Chikmagalur India AIT Adichunchanagiri Institute of Technology Department of Computer Science and Engineering Karnataka Chikmagalur India

ISBN: (纸本)9798350388602

Every nation's basic need is the agricultural products. Disease-ridden leaves have an effect on the nation's agricultural output and financial resources. This research provides a deep learning based plant leaf disease classification and detection method. The website (Plant Village dataset) provided the photos that were used. Since tomatoes, peppers, and potatoes are the most prevalent plant species worldwide and in Iraq specifically, we have used them as examples of certain plant species in our study. There are 20636 photos of plants and their illnesses in this data set. Plant leaf diseases are sorted into 15 class labels in the proposed system using a convolutional neural network (CNN). Of these classes, 12 are dedicated to diseases. Several various plants that were found, including three groups for healthy leaves and various microorganisms including fungus. Consequently, we achieved exceptional precision in both training and testing;specifically, we achieved 98.29% accuracy in training and 98.029% accuracy in testing across all utilized data sets. pattern recognition is necessary in order to diagnose these disorders. Using remote sensing techniques to investigate multi- and hyper-spectral picture captures is a frequent approach in this situation. Techniques that use this strategy frequently use digital image processing capabilities to accomplish their objectives. There are numerous kinds of pattern recognition algorithms that provide accurate disease detection. Plant diseases are currently detected using principal component analysis and back propagation. These algorithms are acquired by neural network training supervision. © 2024 IEEE.

关键词： Convolutional neural networks

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Accurate Laser Line Extraction Algorithm Based on Morphological Features under Strong Interference 12

Accurate Laser Line Extraction Algorithm Based on Morphologi...

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

作者： Wu, Yuanzhe Ye, Liang Hubei Key Laboratory of Optical Information and Pattern Recognition Wuhan430205 China Hubei Engineering Research Center of Video Image and HD Projection Wuhan430205 China School of Electrical and Information Engineering Wuhan Institute of Technology Wuhan430205 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

In outdoor complex lighting environments, laser stripe extraction becomes challenging due to the possibility of strong exposures in the external environment as well as reflective interference on the road surface. In order to overcome these problems, this paper proposes a multi-feature structured light extraction algorithm based on seed neighborhoods, which utilizes multiple seed points and comprehensively considers the laser stripe features, especially the morphological features of the optical stripes in their surrounding neighborhoods, so as to obtain the approximate distribution location of the real laser stripe. Finally, the laser streak extraction is performed by the gradient difference of the optical streak boundary as well as its own brightness. The experimental results show that the method is still effective in extracting laser lines even in high exposure environments where there are interferences such as reflections on the road surface. © 2024 SPIE.

关键词： Luminance

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SkyPole: a geolocation algorithm based on polarized vision without using astronomical ephemerides 16

SkyPole: a geolocation algorithm based on polarized vision w...

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conference on Polarization - Measurement, Analysis, and remote sensing XVI

作者： Kronland-Martinet, Thomas Poughon, Leo Pasquinelli, Marcel Duche, David Serres, Julien R. Viollet, Stephane Aix Marseille Univ CNRS ISM Marseille France Aix Marseille Univ CNRS IM2NP Marseille France Inst Univ France IUF Paris France

ISBN: (纸本)9781510674196;9781510674189

Global Navigation Satellite Systems (GNSS) are widely used due to their easy access to outdoor GNSS signals and their spatial precision. However, such systems are sensitive to jamming and spoofing. Simple and robust navigation strategies can be found in animals deprived by essence of any GNSS system. Studies have shown that animals like bees or ants utilize the sky's polarization pattern for navigation. We recently proposed a method inspired by migratory birds, which calibrate their magnetic compass through the celestial rotation of night stars or the daytime polarization pattern. By considering the temporal properties of the sky's polarization pattern as a relevant navigation information, we developed a bio-inspired method to find the geographical north bearing and the observer's latitude, requiring only skylight polarization observations during day. To reduce the noise susceptibility of our method, we added a pre-processing step using a least square method based on skylight polarization models, and a segmentation process based on a convolutional autoencoder neural network, trained with simulated data.

关键词： Celestial Navigation Celestial compass Polarized vision GPS-less geolocation image processing

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High-resolution sliding-spotlight spaceborne SAR imaging algorithm for Ka-band

High-resolution sliding-spotlight spaceborne SAR imaging alg...

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IET International Radar conference 2023, IRC 2023

作者： Xiao, Feng Jing, Maoqiang He, Dehua Zhang, Guo Zu, JiaGuo Yu, SongBai Sun, HanWei Wang, XinMin Beijing Institute of Radio Measurement Beijing China State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing of Wuhan University Wuhan China Beijing China

ISBN: (纸本)9781839539954

High-band spaceborne SAR offers image-like optics and broadens the application range of microwave remote sensing on space-based platforms. The sliding-spotlight mode fulfils the high-resolution detection requirements for target recognition in remote sensing and has gained significant attention and application in the industry. Given the differences between high-band spaceborne SAR data processing and traditional low-band spaceborne SAR data processing in terms of orbital accuracy analysis, key parameter calculation of imaging, algorithm acceleration, and parallelization, this study conducts an analysis of data accuracy, imaging, and image quality using real data from Luojia-2. The results demonstrate that the proposed imaging process method achieves high-precision imaging of the sliding-spotlight SAR real data from Luojia-2. © The Institution of Engineering & Technology 2023.

关键词： Network security

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