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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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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 153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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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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Speed Matters, a robust infrared and visible image matching method at real-time speed

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JOURNAL OF real-time image processing 2024年第1期21卷 15-15页

作者： Chang, Rong Yang, Chuanxu Zhang, Hang Xie, Housheng Zhou, Chengjiang Pan, Anning Yang, Yang Yunnan Power Grid Corp Yuxi Power Supply Bur Yuxi 653100 Yunnan Peoples R China Yunnan Power Grid Corp Informat Ctr Kunming 650500 Yunnan Peoples R China Yunnan Normal Univ Lab Pattern Recognit & Artificial Intelligence Kunming 650500 Yunnan Peoples R China Baoshan Univ Sch Big Data Baoshan 678000 Peoples R China Yunnan Normal Univ Sch Phys & Elect Informat Kunming 650500 Peoples R China

image matching is a crucial step in executing many complex visual tasks, which involve identifying the same or similar visual patterns across various images. Matching images between infrared and visible becomes quite challenging due to the significant non-linear intensity differences. In this paper, we propose using a lightweight network for feature matching of infrared and visible images, combining global and local feature information, and reducing computational costs, enabling real-time inference on most desktop-level GPUs. To fully leverage the powerful matching capabilities of existing state-of-the-art models, we introduce knowledge distillation to obtain more robust features. Moreover, to address the issue of insufficient datasets for network training in existing methods, we propose using image style transfer techniques to synthesize paired datasets of infrared and visible. Experimental results show that our method achieves results comparable to the most advanced methods in infrared and visible image matching. Furthermore, our method has a significant advantage in inference speed, which is beneficial for tasks that require real-time completion.

关键词： Multimodal real time Feature matching deep learning

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A real-time Speech Enhancement Processor for Hearing Aids in 28-nm CMOS

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IEEE JOURNAL OF SOLID-STATE CIRCUITS 2025年第5期60卷 1830-1843页

作者： Park, Sungjin Lee, Sunwoo Park, Jeongwoo Choi, Hyeong-Seok Lee, Kyogu Jeon, Dongsuk Seoul Natl Univ Res Inst Convergence Sci Dept Intelligence & Informat Seoul South Korea Seoul Natl Univ Interuniv Semicond Res Ctr Seoul South Korea Sungkyunkwan Univ Dept Elect & Comp Engn Suwon 16419 South Korea ElevenLabs New York NY USA Seoul Natl Univ Dept Intelligence & Informat Seoul 08826 South Korea Seoul Natl Univ Interdisciplinary Program Artificial Intelligence Seoul 08826 South Korea

Speech enhancement (SE) plays a key role in many audio-related applications by removing noise and enhancing the quality of human voice. Recent deep learning-based approaches provide high-quality SE, but real-time processing of those algorithms is challenging in resource-constrained devices due to high computational complexity. In this article, we present an energy-efficient real-time SE processor aimed at hearing aids. To implement high-quality SE with a very limited power budget, various algorithm and hardware optimization techniques are proposed. Our SE algorithm adaptively allocates computational resources to each region in the input feature domain depending on their importance, reducing overall computations by 29.7%. Along with 4-bit channel-wise logarithmic quantization, the processor adopts a reconfigurable multiplier-less processing element (PE) that supports both pre-/post-processing and neural network layers, resulting in a 21.5% area reduction. In addition, the design employs efficient scheduling and input buffering schemes to reduce on-chip memory access by 70.8%. Fabricated in a 28-nm CMOS process, our design consumes only 740 mu W at 2.5 MHz with a total latency of 39.96 ms, satisfying the real-time processing constraints. In addition, our approach demonstrated higher SE quality than prior art in both objective and subjective evaluations.

关键词： Neural networks Hearing aids real-time systems Convolution Optimization Decoding Computational modeling Digital hearing aids multiplier-less processing element (PE) neural network processor reconfigurable architecture speech enhancement (SE)

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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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Agry: a comprehensive framework for plant diseases classification via pretrained EfficientNet and convolutional neural networks for precision agriculture

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MULtimeDIA TOOLS AND APPLICATIONS 2024年第24期83卷 64813-64851页

作者： Saleki, Sheida Tahmoresnezhad, Jafar Urmia Univ Technol Fac Informat Technol & Comp Engn Orumiyeh Iran

Diagnosing plant diseases is a vital issue in maintaining and developing of agricultural products. These diseases occur with changes in the tissue of different parts of plants. While the previous researches have only been conducted on certain species of plants and specific parts, we propose a comprehensive approach that has reached a high accuracy in diagnosing and classifying the disease of offending plant species by examining their different parts, including leaves, fruits, tree trunks, and seeds. We extract features from different layers of pre-trained AlexNet, ResNet50, VGG16, EfficientNetB0, EfficientNetB3 and EfficientNetB7 deep models with a spatial attention module to classify samples with an SVM classifier with RBF kernel. In order to automate the detection of plant diseases by manned or unmanned agricultural machines, our proposed Agry requires only 0.04089 seconds for image processing and decision making in real time. Also, due to the use of transfer learning, the cost of building the proposed model, including time and resources, is minimized. The results of the tests show a significant improvement compared to the previous works, and in most cases the classification is done without errors.

关键词： Precision agriculture Plant disease Machine vision deep learning Transfer learning deep convolutional neural networks Pre-trained models

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