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Shallow multiplexing and multiscale dilation convolution combined attention based oriented object detection in remote sensing images

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DIGITAL signal processing 2025年 156卷

作者： Wang, Jiangtao Shi, Jiawei Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Anhui Prov Key Lab Intelligent Comp & Applicat Huaibei 235000 Peoples R China

remote sensing images are becoming increasingly important in many areas of life because of the valuable information they provide. However, detecting objects in these images remains a difficult task due to their complex and variable characteristics, such as size, scale, and orientation. Moreover, there is a growing demand for efficient and speedy detection methods in practical applications. Therefore, in this paper, we propose a framework for oriented object detection in remote sensing images based on shallow multiplexing and multiscale dilation convolution combined attention. To achieve a lightweight network structure, we utilize ResNet18 as the backbone network. First, a shallow multiplexing module (SM) is designed to improve the utilization of detailed information in the shallow layer of the network. It enhances the interaction between the shallow and deep layers, resulting in a richer representation of network features. Second, a multiscale dilation convolution combined attention module (MDCA) is proposed to prioritize contextual information by using convolution with different dilation rates. This guides the network to focus more on the object information in remote sensing images. Then, the dilated encoder (DE) is employed at the feature fusion stage to enhance the semantic information of the context and produce a feature map with multiple receptive fields. Finally, the log2 loss function is applied to improve the training results. The experiments are being conducted on three publicly available remote sensing image datasets, and the results demonstrate that the proposed algorithm outperforms other algorithms in terms of detection performance on these datasets. Code is available at https://***/sbsfsum/SM-and-MDCA.

关键词： Object detection remote sensing images SM MDCA Dilated convolution

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An LFM-Based Semiblind Synchronization Method for Bistatic Radar Imaging

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2024年 17卷 15089-15101页

作者： Park, James Raj, Raghu G. US Naval Res Lab Radar Div Washington DC 20375 USA

Bistatic radar provides an additional degree of freedom in the observing geometry for imaging by using a spatially separated transmitter and receiver. This capability allows the capture of scattering phenomena at various bistatic sensing angles, which helps to interpret the physical characteristics of a target or scene for remote sensing and radar applications. A crucial problem associated with bistatic radar is the synchronization of time and frequency between the transmitter and receiver for coherent image formation. In this article, a model-based method of synchronization, called semiblind synchronization, is introduced for maintaining the phase coherence of a bistatic radar system for range-Doppler processing. Specifically, semiblind synchronization refers to bistatic synchronization where the direct path is not available or where the global positioning system signal is not accurate enough, and where prior knowledge of the transmit waveform and target location are exploited during a coherent processing interval. The method focuses on error estimation and compensation based on understanding the impact of synchronization errors on the received signals. After detailing our system model and semiblind synchronization method, we conduct a simulation study for assessing the effectiveness of our method by first introducing a variety of synchronization errors, including chirp rate mismatch, and time and frequency offsets. Scattering responses from a point target are simulated, and high-range resolution profiles and range-Doppler images are formed to validate the proposed method. The simulation results demonstrate that a linear frequency modulation waveform enables us to estimate and compensate the synchronization errors considered in this article and improve the overall quality of the bistatic range-Doppler image. Our synchronization technique has potentially broad applicability in emerging remote sensing applications using bistatic radar systems.

关键词： Synchronization Receivers Bistatic radar Radar imaging Transmitters Global Positioning System Radar range-Doppler processing stretch processing

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Editorial for the Special Issue "Machine Learning in Computer Vision and image sensing: Theory and Applications"

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SENSORS 2024年第9期24卷 2874页

作者： Chakraborty, Subrata Pradhan, Biswajeet Univ New England Sch Sci & Technol Armidale NSW 2351 Australia Univ Technol Sydney Fac Engn & IT Ctr Adv Modelling & Geospatial Informat Syst CAMGI Sch Civil & Environm Engn Sydney NSW 2007 Australia Griffith Univ Griffith Business Sch Nathan Qld 4111 Australia Univ Kebangsaan Malaysia Inst Climate Change Earth Observat Ctr Bangi 43600 Selangor Malaysia

Machine learning (ML) models have experienced remarkable growth in their application for multimodal data analysis over the past decade [1]. The diverse applications of ML models span domains such as medical image [2,3,4] and signal processing [5,6], remote sensing for earth observation and monitoring [7,8,9], the detection of daily human activities [10,11], and many more. ML models play a significant role in supporting computer vision and image-sensing applications, helping to unravel complex and real-world challenges. Recent developments in ML empower us to better analyse image and sensor data, motivating extensive research initiatives aimed at addressing applied challenges in multiple domains, including healthcare, agriculture, defence, remote sensing, earth observation, and autonomous navigation.

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MULTIMODAL TRANSFORMER USING CROSS-CHANNEL ATTENTION FOR OBJECT DETECTION IN remote sensing imageS 31

MULTIMODAL TRANSFORMER USING CROSS-CHANNEL ATTENTION FOR OBJ...

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2024 International conference on image processing

作者： Bahaduri, Bissmella Ming, Zuheng Feng, Fangchen Mokraoui, Anissa Univ Sorbonne Paris Nord Lab L2TI Villetaneuse France

ISBN: (纸本)9798350349405;9798350349399

Object detection in remote sensing images (RSI) is a critical task for numerous applications in Earth Observation (EO). Differing from object detection in natural images, object detection in remote sensing images faces challenges of scarcity of annotated data and the presence of small objects represented by only a few pixels. Multi-modal fusion has been determined to enhance the accuracy by fusing data from multiple modalities such as RGB, infrared (IR), lidar, and synthetic aperture radar (SAR). To this end, the fusion of representations at the mid or late stage, produced by parallel subnetworks, is dominant, with the disadvantages of increasing computational complexity in the order of the number of modalities and the creation of additional engineering obstacles. Using the cross-attention mechanism, we propose a novel multi-modal fusion strategy for mapping relationships between different channels at the early stage, enabling the construction of a coherent input by aligning the different modalities. By addressing fusion in the early stage, as opposed to mid or late-stage methods, our method achieves competitive and even superior performance compared to existing techniques. Additionally, we enhance the SWIN transformer by integrating convolution layers into the feed-forward of non-shifting blocks. This augmentation strengthens the model's capacity to merge separated windows through local attention, thereby improving small object detection. Extensive experiments prove the effectiveness of the proposed multimodal fusion module and the architecture, demonstrating their applicability to object detection in multimodal aerial imagery. Our code is available at here.

关键词： Multimodal fusion cross-channel attention convolutional shifting window object detection remote sensing imagery

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Koopman Ensembles for Probabilistic Time Series Forecasting 32

Koopman Ensembles for Probabilistic Time Series Forecasting

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32nd European signal processing conference (EUSIPCO)

作者： Frion, Anthony Drumetz, Lucas Tochon, Guillaume Dalla Mura, Mauro El Bey, Abdeldjalil Aissa IMT Atlantique Lab STICC Brest France LRE EPITA Le Kremlin Bicetre France Univ Grenoble Alpes CNRS Grenoble INP Inst Univ FranceGIPSA Lab Grenoble France

ISBN: (纸本)9789464593617;9798331519773

In the context of an increasing popularity of data-driven models to represent dynamical systems, many machine learning-based implementations of the Koopman operator have recently been proposed. However, the vast majority of those works are limited to deterministic predictions, while the knowledge of uncertainty is critical in fields like meteorology and climatology. In this work, we investigate the training of ensembles of models to produce stochastic outputs. We show through experiments on real remote sensing image time series that ensembles of independently trained models are highly overconfident and that using a training criterion that explicitly encourages the members to produce predictions with high inter-model variances greatly improves the uncertainty quantification of the ensembles.

关键词： Dynamical systems Koopman operator Uncertainty quantification remote sensing Sentinel-2

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Triplet Network with Autoencoder for remote sensing image scene recognition and classification

Triplet Network with Autoencoder for Remote sensing image sc...

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2024 International conference on image, signal processing, and Pattern Recognition, ISPP 2024

作者： Hu, Fangchun Dai, Yifeng Wu, Lie Anhui University Anhui China Chuzhou University Anhui China

ISBN: (纸本)9781510680425

remote sensing image scene categorization is a crucial process that involves assigning semantic labels to images based on their content,which has wide-ranging applications in a number of *** inherent potential of deep neural networks to learn attractive features has substantially sped up progress in the field, resulting in considerable breakthroughs in remote sensing and scene classification powered by deep ***, the existing literature lacks a comprehensive evaluation of research findings in deep learning for remote sensing image scene categorization, despite the notable advancements in this *** CNN has shown remarkable efficacy in extracting image features, ranging there are persistent challenges that hinder its optimal performance in the domain of categorization of images from data collected from remote *** intrinsic attributes linked to remote sensing image data, such as intraclass variability and inter-class similarities, result in a notable decrease in the classification accuracy of the ***, the discernible impact of noise and interference further exacerbates limitations in CNN performance within this specific *** response to these challenges, this study introduces an innovative classification methodology that seamlessly integrates a triplet network and ***, the paper systematically conducts a rigorous comparative assessment, evaluating the performance of diverse image classification models across various remote sensing *** holistic approach aims to contribute to the evolving landscape of remote sensing image scene classification by addressing existing gaps and advancing our understanding of effective model architectures. © 2024 SPIE.

关键词： Deep neural networks

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Computational methods to retrieve soil moisture using remote sensing data: A review 7

Computational methods to retrieve soil moisture using remote...

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IEEE 7th International conference on Advanced Technologies, signal and image processing (ATSIP)

作者： Inoubli, Raja Constantino-Recillas, Daniel Enrique Monsivais-Huertero, Alejandro Farah, Lilia Bennaceur Farah, Imed Riadh Natl Sch Comp Sci Labratoire RIADI Manouba 1001 Tunisia Tecnol Nacl Mexico TES Ecatepec Mexico City DF Mexico Inst Politecn Nacl ESIME Ticoman Mexico City DF Mexico Preparatory Inst Engn Studies El Manar LTSIRS Lab Manar Tunisia Natl Inst Engn Sci Tunis Manar Tunisia

ISBN: (纸本)9798350351491;9798350351484

Recently, soil moisture (SM) retrieval has become the first interest of the scientific community due to the increasing demand for water resources in several fields, especially the agricultural sector. A lack of such resources has been detected along with the increasing climate changes and disasters occurring such as drought. It has become very essential to search for efficient approaches and data sources that can serve effectively the SM retrieval goals. This paper is a literature review set to identify the strengths and weaknesses of the most relevant works in the area of SM retrieval and remote sensing (RS) published from 2017 to 2024. Summarizing the selected studies serves to detect the relevant information that tackles the SM retrieval challenges. Thus, creating new horizons for new research. One of the main fields of interest is SM retrieval over agriculture based on the synergistic use of Sentinel-1 (S-1) and Sentinel-2 (S-2) images. High accuracy has been achieved by combining different approaches such as backscattering models and Machine Learning (ML) algorithms (e.g., AIEM and NN algorithms). The application of such a combination is highly recommended due to its promising potential.

关键词： Soil moisture arid and semi-arid regions backscattering models Machine learning Change detection hybrid approaches remote sensing retrieval

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Assessment of a UAV Radar System for Reconstructing Vertical Structure of Forests by Single Pass

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2025年 63卷

作者： Zhang, Ping Wu, Zhipeng Li, Zhen Huang, Lei Liu, Chang Gao, Shuo Zhou, Jianmin Qiao, Haiwei Zhang, Shiqun Chinese Acad Sci Aerosp Informat Res Inst Int Res Ctr Big Data Sustainable Dev Goals Beijing Peoples R China Chinese Acad Sci Aerosp Informat Res Inst Key Lab Digital Earth Sci Beijing Peoples R China Nufront Beijing Technol Grp Co Ltd Beijing 100084 Peoples R China

Forests are the most extensive terrestrial ecosystems in the world. Their vertical structure information not only reflects the spatial structure characteristics of the forest but also the physiological and ecological processes. The existing studies on forest vertical structure through remote sensing often encounter challenges such as high costs, difficulties in acquiring effective data, or complexities in data processing. In this study, a new technology for detecting the vertical structure of vegetation is proposed and validated, which can obtain the vertical structure of vegetation by single flight. In order to adopt the new technology, a radar system was developed, incorporating a modular design scheme that emphasizes high integration and lightweight characteristics, and it was deployed on an unmanned aerial vehicle (UAV) flight platform. It consists of a main control unit, a signal processing unit, and a data recording unit, which weighs only 0.92 kg in total. Meanwhile, a novel algorithm is proposed to reconstruct the vertical structure of targets, effectively addressing critical challenges in UAV radar signal processing, such as strong system coupled signal, significant noise in radar signals, and high sidelobes in images. In order to validate the capability of the new technology, UAV flight experiments, as well as in situ observation, were carried out in typical vegetation areas. The proposed algorithm was used to process the acquired radar echoes, yielding a root-mean-square error (RMSE) of 1.33 m for the vegetation height compared to the ground synchronous measurement.

关键词： Forests Radar Autonomous aerial vehicles Radar antennas Spaceborne radar Radar imaging Vegetation mapping Laser radar Radar remote sensing Costs Forest canopy forest height radar image unmanned aerial vehicle (UAV) radar vertical structure

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Visual Cardiac signal Classifiers: A Deep Learning Classification Approach for Heart signal Estimation From Video

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IEEE ACCESS 2024年 12卷 144377-144394页

作者： Moustafa, Mohamed Farooq, Muhammad Ali Elrasad, Amr Lemley, Joseph Corcoran, Peter Univ Galway Dept Elect & Elect Engn Imaging Lab C3I Galway H91 TK33 Ireland FotoNation Sensing Team Galway H91 V0TX Ireland

Heart rate is a crucial metric in health monitoring. Traditional computer vision solutions estimate cardiac signals by detecting physical manifestations of heartbeats, such as facial discoloration caused by blood oxygenation changes, from subject videos using regression methods. As continuous signals are more complex and expensive to de-noise, this study introduces an alternative approach, employing end-to-end classification models to remotely derive a discrete representation of cardiac signals from face videos. These visual cardiac signal classifiers are trained on discretized cardiac signals, a novel pre-processing method with limited precedent in health monitoring literature. Consequently, various methods to convert continuous cardiac signals into binary form are presented, and their impact on training is evaluated. An implementation of this approach, the temporal shift convolutional attention binary classifier, is presented using the regression-based convolutional attention network architecture. The classifier and a baseline regression model are trained and tested using publicly available and locally collected datasets designed for heart signal detection from face video. The model performance is then assessed based on the heart rate error from the extracted cardiac signals. Results show the proposed method outperforms the baseline on the UBFC-rPPG dataset, reducing cross-dataset root mean square error from 2.33 to 1.63 beats per minute. However, both models struggled to generalize to the PURE dataset, with root mean square errors of 12.40 and 16.29 beats per minute, respectively. Additionally, the proposed approach reduces the computational complexity of model output post-processing, enhancing its suitability for real-time applications and deployment on systems with restricted resources.

关键词： Convolutional neural networks Heart rate Computer vision Training Estimation remote sensing Visualization Standards Monitoring Feature extraction Deep learning Biomedical signal processing Photoplethysmography deep learning image classification remote monitoring remote sensing biomedical signal processing heart rate photoplethysmography

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Open-Set remote sensing Object Detection Using Edge Information Extraction 2

Open-Set Remote Sensing Object Detection Using Edge Informat...

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2nd IEEE International conference on signal, Information and Data processing, ICSIDP 2024

作者： Li, Xiaozhe Dang, Sihang Sun, Yifei Jiang, Xiaoyue Gui, Shuliang Feng, Xiaoyi Northwestern Polytechnical University School of Electronics and Information Xi'an China Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing China

ISBN: (纸本)9798331515669

Typically, remote sensing object detection is limited to a closed-set detection environment. A problem is that when detecting the new untrained but valuable objects, they are incorrectly classified as known classes or background. In this paper, a method is proposed for open-set detection of remote sensing objects, which assigns robust pseudo-labels to unknown classes and trains the network to recognize new classes. The main idea is to combine the feature information of remote sensing objects and select regions in the image that are likely to contain unknown classes to form pseudo-labels. Through supervised learning, the network can distinguish and detect both known and unknown classes. remote sensing objects are observed from an Earth observation perspective, with rich edge information. Pseudo-labels for unknown classes are obtained using image convolution features and object edge information. Experimental results show that this method outperforms existing methods in open-set target detection for remote sensing images. © 2024 IEEE.

关键词： Optical remote sensing

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