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Trends and Applications of On-Board image processing for Earth Observation Nanosatellites: A Systematic Review

INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2025年第4期26卷 1989-2019页

作者： Chanoui, Mohammed Alae Khalil, Imane Sbihi, Mohammed Ismaili, Zine El Abidine Alaoui Guennoun, Zouhair Mohammed V Univ Rabat Higher Sch Technol Sale Lab Syst Anal Informat Proc & Ind Management LASTI Rabat Morocco Mohammed V Univ Rabat Univ Ctr Res Space Technol CURTS Mohammadia Sch Engineers Rabat Morocco Mohammed V Univ Rabat Mohammadia Sch Engineers Smart Commun Res Team ERSC Rabat Morocco Mohammed V Univ Rabat Natl Sch Comp Sci & Syst Anal Informat Commun & Embedded Syst Team ICES Rabat Morocco

On-board image processing represents a practical application for satellite imagery, aiming to optimize or prioritize data transmission to ground stations, thereby enhancing bandwidth utilization. In addition to their low cost and reduced development time, nanosatellites have experienced significant growth in applications and capabilities as technology advances. Presently, the implementation of on-board image processing has been successfully tested on real nanosatellite missions. Earth observation stands as the most utilized application for nanosatellites equipped with on-board imagers, and integrating image processing in those nanosatellites would enhance the overall performance of the mission. Hence, this article is a systematic review that investigates the current advancements and trends in terms of applications and technologies in the context of image processing on-board nanosatellites. To conduct the study systematically, Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines were utilized. After the search and screening process, 73 relevant publications were analyzed to extract pertinent data for the review. The findings outlined a particular interest toward the application of deep learning methods on-board nanosatellites, especially for applications such as image classification or image segmentation.

关键词： Nanosatellites Earth observation image processing Systematic review PRISMA

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Evaluation of Transfer learning for Visual Multiple Target Tracking 1

Evaluation of Transfer Learning for Visual Multiple Target T...

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1st International Conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Jadhav, Anisha Anil California State University Department of Computer Science Monterey Bay Seaside United States

ISBN: (纸本)9798331518578

deep learning has revolutionized high-level image processing tasks, notably image classification and segmentation, by effectively handling multi-dimensional features in image space. This report investigates the application of transfer learning, utilizing features extracted from a pre-trained VGG deep neural network to visual multiple-target tracking (MTT). Traditional feature detectors like the Lucas-Kanade Tracker and Good Features to Track (GFTT) algorithm are computationally efficient but limited in dynamic, cluttered environments, such as those encountered by unmanned aerial systems (UAS). Replacing GFTT with deep feature detectors from VGG is proposed to assess tracking accuracy and improve computational efficiency. Our experiments reveal that, though promising, deep feature extraction results in lower frame rates and less smooth trajectories than traditional methods. This study highlights the potential and current limitations of integrating deep learning features into real-time visual tracking systems. Plans include optimizing the deep learning model for real-time processing, automating filter selection for feature extraction, and exploring additional pre-trained networks to improve tracking performance and efficiency. © 2025 IEEE.

关键词： image segmentation

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TinyCount: an efficient crowd counting network for intelligent surveillance

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JOURNAL OF real-time image processing 2024年第4期21卷 1-13页

作者： Lee, Hyeonbeen Lee, Jangho Incheon Natl Univ Dept Comp Sci & Engn Incheon 22012 South Korea

Crowd counting, the task of estimating the total number of people in an image, is essential for intelligent surveillance. Integrating a well-trained crowd counting network into edge devices, such as intelligent CCTV systems, enables its application across various domains, including the prevention of crowd collapses and urban planning. For a model to be embedded in edge devices, it requires robust performance, reduced parameter count, and faster response times. This study proposes a lightweight and powerful model called TinyCount, which has only 60k parameters. The proposed TinyCount is a fully convolutional network consisting of a feature extract module (FEM) for robust and rapid feature extraction, a scale perception module (SPM) for scale variation perception and an upsampling module (UM) that adjusts the feature map to the same size as the original image. TinyCount demonstrated competitive performance across three representative crowd counting datasets, despite utilizing approximately 3.33 to 271 times fewer parameters than other crowd counting approaches. The proposed model achieved relatively fast inference times by leveraging the MobileNetV2 architecture with dilated and transposed convolutions. The application of SEblock and findings from existing studies further proved its effectiveness. Finally, we evaluated the proposed TinyCount on multiple edge devices, including the Raspberry Pi 4, NVIDIA Jetson Nano, and NVIDIA Jetson AGX Xavier, to demonstrate its potential for practical applications.

关键词： Crowd counting Lightweight network Intelligent surveillance Artificial intelligence deep learning Edge machine learning

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A deep learning based architecture for multi-class skin cancer classification

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Multimedia Tools and Applications 2024年第39期83卷 87105-87127页

作者： Mushtaq, Snowber Singh, Omkar Srinagar190006 India

One of the deadliest forms of skin cancer is malignant melanoma, developed by aberrant melanocyte cell development. Efficient diagnostic procedures are essential due to the rising prevalence of skin illnesses resulting from several variables such as infections, allergies, dietary habits, chemical exposure, genetic sensitivities, and lifestyle changes. An increasing number of deep Convolutional Neural Network (DCNN) prototypes are used for the computerized identification of skin cancer using dermoscopic images. The research aims to develop a detailed classification framework for the multi-class categorization of skin cancer carcinoma at an earlier phase using Hair removal image processing Technique and Ensemble Visual Geometry Group-16 (EVGG-16) based on a Convolution Neural Network. The main idea is to set different initializations to the same VGG-16 model three times and then to Ensemble the results. The Experimental results show that the VGG-16 model receives an average accuracy of 87% and an F1 score of 86%. The proposed Ensemble Visual Geometry Group-16 (EVGG-16) model receives an average accuracy of 88% and an F1-score of 88% without the hair removal image processing technique;the same model receives an average accuracy of 89% and an F1-score of 88% after applying the hair removal image processing technique. The publicly accessible HAM1000 dataset was employed to experiment;it includes processed images belonging to seven classes to evaluate the model's effectiveness. In conclusion, our proposed Ensemble model with Hair removal technique performs better than other cutting-edge deep-learning techniques and skilled dermatologists. Furthermore, this might be a trustworthy real-time teaching resource for medical students in a dermatology program. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： learning systems

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Adaptive Feature Aggregation Centric Enhance Network for Accurate and Fast Monocular 3-D Object Detection

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2024年 73卷

作者： Lin, Peng-Wei Hsu, Chih-Ming Natl Taipei Univ Technol Inst Mech & Elect Engn Taipei 10608 Taiwan Natl Taipei Univ Technol Dept Mech Engn Taipei 10608 Taiwan

Three-dimensional object detection is crucial in autonomous driving. Monocular 3-D object detection has become a popular area of research in autonomous driving because of its ease of deployment and cost-effectiveness. In real-world autonomous driving, detectors should be both real time and accurate. These features can be achieved using deep learning (DL). A one-stage center-based object detector is suitable for real-world applications. However, in center-based object detectors, object-centric estimation plays an important role because it significantly influences detection results. To address this issue, we propose a real-time monocular 3-D object detection neural network called the adaptive feature aggregate centric enhance network. The proposed model is an anchor-free and center-based method. To improve accuracy while maintaining inference speed, we propose an adaptive feature aggregation network that aggregates multiscale features by weighting. Furthermore, we propose a centric enhanced module for heatmap prediction to improve the accuracy of object localization and classification. Our model can achieve 34.48 fps using an Nvidia RTX3070 graphy processing unit (GPU). Extensive experiments on the KITTI benchmark demonstrate that our method achieves good average precision (AP) for cars and pedestrians.

关键词： Three-dimensional displays Heating systems Feature extraction Accuracy Detectors Head Location awareness Convolution Autonomous vehicles Aggregates 3-D object detection attention mechanism autonomous driving deep learning (DL) monocular image real time

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HIDS-IoMT: A deep learning-Based Intelligent Intrusion Detection System for the Internet of Medical Things

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IEEE ACCESS 2025年 13卷 32863-32882页

作者： Berguiga, Abdelwahed Harchay, Ahlem Massaoudi, Ayman Jouf Univ Coll Comp & Informat Sci Dept Comp Sci Sakaka 72388 Saudi Arabia Univ Carthage Mediatron Res Lab Higher Sch Commun Ariana 1054 Tunisia

The expansion of the Internet of Medical Things (IoMT) has enhanced the accuracy, real-time functionality, connectivity, and intelligence of medical examination practices. However, increased interconnectivity of medical equipment has rendered IoMT networks susceptible to several cyberattacks, particularly the Distributed Denial of Service (DDoS) attacks. Current intrusion detection systems (IDS) are insufficiently focused to address and counteract these advanced threats. Furthermore, due to the dynamic nature of IoMT traffic, IDS has considerable difficulty preserving its current threat detection capabilities. This study presents a hybrid deep learning-based intrusion detection system for IoMT networks (HIDS-IoMT). The proposed model hybridizes the Convolutional Neural Network (CNN) for feature extraction and the Long Short Term Memory neural network (LSTM) for sequence data prediction. We implement the designed IDS on a Raspberry Pi device using a fog computing architecture, enabling decentralized processing closer to IoMT devices, thereby enhancing responsiveness and reducing latency. We evaluate the suggested approach for intrusion detection using the IoTID20 and the Edge-IIoTset datasets. These datasets comprise a substantial and varied assortment of traffic flows from actual DDoS attacks, including SYN floods, UDP floods, HTTP floods, and others. We evaluated our novel methodology against distinct DoS attacks such as DDoS, SQL injection, Vulnerability scanner, Scan Host Port, Mirai, etc. Our proposed model attains an accuracy of 99.92%, a precision of 99.91%, a recall rate of 99.99%, and an F1-score of 99.95%, outperforming the latest in advanced methodologies.

关键词： Accuracy image edge detection Convolutional neural networks Intrusion detection Denial-of-service attack Computer architecture Training Telecommunication traffic real-time systems Neural networks Internet of Medical Things DDoS attacks intrusion detection system deep learning convolutional neural network long short term memory neural network

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Demystifying SAR with attention

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EXPERT SYSTEMS WITH APPLICATIONS 2025年 276卷

作者： Patnaik, Nitesh Raj, Rishi Misra, Indranil Kumar, Vinod Kalinga Inst Ind Technol KIIT Univ Sch Comp Engn Bhubaneswar 751024 Odisha India Indian Inst Management Visakhapatnam Informat Syst Visakhapatnam India Indian Space Res Org Space Applicat Ctr Ahmadabad India Indian Inst Technol BHU Dept Comp Sci & Engn Varanasi 221005 Uttar Pradesh India

Synthetic Aperture Radar (SAR) imagery is indispensable for earth observation, offering the ability to capture data under challenging conditions such as cloud cover and darkness. However, its grayscale format and speckle noise hinder interpretability and pose significant challenges for traditional processing methods. This study introduces an innovative framework for SAR image colorization, leveraging an Attention-Based WGAN-GP (Wasserstein GAN with Gradient Penalty). The model incorporates multi-head self-attention mechanisms to enhance feature extraction, capture long-range dependencies, and dynamically suppress noise through a novel variance-based attention adjustment mechanism. Extensive evaluations on Sentinel-1 and Sentinel-2 datasets across diverse terrains, including agriculture, urban areas, barren land, and grasslands, demonstrate the model's superiority over existing approaches. It achieves an LPIPS score of 0.27, SSIM of 0.76, and an average inference time of 0.22 s, showcasing its ability to preserve spatial coherence and perceptual quality even in complex, noisy environments. This capability enables real-time applications in disaster management, flood monitoring, and urban planning, providing actionable insights and advancing the state-of-the-art in SAR image processing.

关键词： Attention deep learning Generative adversarial networks image colorization image restoration Multihead attention Noise SAR Sentinel

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A Video Frame Extrapolation Scheme Using deep learning-Based Uni-Directional Flow Estimation and Pixel Warping

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IEEE ACCESS 2023年 11卷 105885-105891页

作者： Ban, Tae-Won Gyeongsang Natl Univ Dept Intelligent Commun Engn Jinju 52828 Gyeongsangnam South Korea

This paper investigates video frame extrapolation, which can predict future frames from current and past frames. Although there have been many studies on video frame extrapolation in recent years, most of them suffer from the unsatisfactory image quality of the predicted frames such as severe blurring because it is difficult to predict the movement of future pixels for multi-modal video frames, especially with fast changing frames. An additional process such as frame alignment or recurrent prediction can improve the quality of the predicted frames, but it hinders real-time extrapolation. Motivated by the significant progress in video frame interpolation using deep learning-based flow estimation, a simplified video frame extrapolation scheme using deep learning-based uni-directional flow estimation is proposed to reduce the processing time compared to conventional video frame extrapolation schemes without compromising the image quality of the predicted frames. In the proposed scheme, the uni-directional flow is first estimated from the current and past frames through a flow network consisting of four flow blocks and the current frame is forward-warped through the estimated flow to predict a future frame. The proposed flow network is trained and evaluated using the Vimeo-90K triplet dataset. The performance of the proposed scheme is analyzed using the trained flow network in terms of prediction time as well as the similarity between predicted and ground truth frames such as the structural similarity index measure and mean absolute error of pixels, and compared to that of the state-of-the-art schemes such as Iterative and cycleGAN schemes. Extensive experiments show that the proposed scheme improves prediction quality by 2.1% and reduces prediction time by 99.7% compared to the state-of-the-art scheme.

关键词： Video frame extrapolation video frame prediction flow estimation flow network deep learning

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Enhancing passive gamma emission tomography data with deep learning

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ANNALS OF NUCLEAR ENERGY 2024年 204卷

作者： Sanchez-Belenguer, Carlos Casado-Coscolla, Alvaro Wolfart, Erik Joint Res Ctr European Commiss Via Enrico Fermi 2749 I-21027 Ispra VA Italy Seidor Italy SRL Contract European Commiss Ispra Italy

In this paper, we address the problem of generating and enhancing Passive Gamma Emission Tomography (PGET) data from a deep learning perspective. The PGET instrument has been developed for the verification of spent nuclear fuel and relies on image reconstruction and analysis algorithms to detect missing or substituted fuel pins. Such techniques are sensitive to the quality of the input data: noisy or incomplete sinograms yield to poor reconstructions and, consequently, to low-confidence results. The development and validation of these algorithms is based on complex Monte Carlo simulations that are time-consuming and computationally-demanding. We propose the use of Convolutional Neural Networks (CNNs) for enhancing PGET data. Our technique learns the mapping between incomplete or noisy sinograms and their corresponding full representation. It effectively exploits the high degree of redundancy of the measurements, i.e . the contribution of a single pin can be observed from many different directions, to learn the underlying model of the data and to make informed predictions. The two main applications of our approach are: (1) accelerating Monte Carlo simulations and (2) pre-processing real measurements to enhance them before running the standard image reconstruction and analysis techniques. The experimental evaluation was performed with both, simulated and real measurements. Results show how effectively CNNs can learn and exploit the structure of the data. For the two use cases evaluated, denoising sinograms and inpainting incomplete ones, our technique achieved state-of-the-art performance with execution times in the order of milliseconds.

关键词： Passive gamma emission tomography deep learning Monte Carlo simulations

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A survey on few-shot class-incremental learning

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NEURAL NETWORKS 2024年 169卷 307-324页

作者： Tian, Songsong Li, Lusi Li, Weijun Ran, Hang Ning, Xin Tiwari, Prayag Chinese Acad Sci Inst Semicond Beijing 100083 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 100049 Peoples R China Univ Chinese Acad Sci Sch Integrated Circuits Beijing 100083 Peoples R China Beijing Key Lab Semicond Neural Network Intellige Beijing 100083 Peoples R China Old Dominion Univ Dept Comp Sci Norfolk VA 23529 USA Halmstad Univ Sch Informat Technol S-30118 Halmstad Sweden

Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective.

关键词： Few-shot learning Class-incremental learning Catastrophic forgetting Overfitting Performance evaluation

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