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2024 International Conference on remote sensing, Mapping, and Geographic Information Systems, RSMG 2024

作者： Yuan, Jieran Song, Beibei Sun, Wenfang School of Information Engineering Chang’an University Xi’an710018 China School of Aerospace Science and Technology Xidian University Xi’an710068 China

ISBN: (纸本)9781510685826

Capturing motion vehicle information from satellite videos is crucial for real-time traffic monitoring and emergency response. However, vehicles in satellite videos are small in size, lack detailed textural features and are easily obscured by complex backgrounds. Traditional frame differencing and background subtraction methods often lead to a high number of false positives and false negatives, while deep learning methods struggle to meet real-time processing requirements. To strike a balance between detection performance and processing efficiency, a real-time vehicle detection method combining hole features extracted from frame differencing and motion direction estimation is proposed. Initially, a multiframe image accumulation (MIA) strategy is employed to enhance the visibility of vehicle targets and suppress background noise. Subsequently, the hole features of moving vehicles are extracted by differencing adjacent accumulated images, leading to the creation of a hole model for coarse detection of moving vehicles. Then, by estimating the motion direction of vehicles and enforcing direction consistency constraints, hole matching of neighboring vehicles in motion is achieved to improve the detection accuracy. Finally, a novel region extraction algorithm that integrates target hole features and motion direction information is designed to effectively suppress false positives generated by background noise. This method exhibits superior detection performance on the VISO benchmark dataset, achieving a recognition accuracy of 89.5% while meeting real-time processing requirements with an average processing time of only 0.04 seconds per frame, ensuring both detection performance and processing efficiency. © 2024 SPIE.

关键词： image segmentation

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Detecting the Distribution of Callery Pear (Pyrus calleryana) in an Urban US Landscape Using High Spatial Resolution Satellite imagery and Machine Learning

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remote sensing 2025年第8期17卷 1453-1453页

作者： Krohn, Justin He, Hong Matisziw, Timothy C. Knapp, Lauren S. Pile Fraser, Jacob S. Sunde, Michael Univ Missouri Inst Data Sci & Informat Columbia MO 65211 USA Univ Missouri Inst Data Sci & Informat Sch Nat Resources Columbia MO 65211 USA Univ Missouri Inst Data Sci & Informat Dept Geog Dept Civil & Environm Engn Columbia MO 65211 USA USDA Forest Serv Columbia MO 65211 USA Univ Missouri Sch Nat Resources MU Extens Columbia MO 65211 USA

Using Planetscope imagery, we trained a random forest model to detect Callery pear (Pyrus calleryana) throughout a diverse urban landscape in Columbia, Missouri. The random forest model had a classification accuracy of 89.78%, a recall score of 0.693, and an F1 score of 0.819. The key hyperparameters for model tuning were the cutoff and class-weight parameters. After the distribution of Callery pear was predicted throughout the landscape, we analyzed the distribution pattern of the predictions using Ripley's K and then associated the distribution patterns with various socio-economic indicators. The analysis identified significant relationships between the distribution of the predicted Callery pear and population density, median household income, median year the housing infrastructure was built, and median housing value at a variety of spatial scales. The findings from this study provide a much-needed method for detecting species of interest in a heterogenous landscape that is both low cost and does not require specialized hardware or software like some alternative deep learning methods.

关键词： Pyrus calleryana Decne digital image processing spatial clustering species inventory Planetscope

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OB-ConvLSTM: A sequential remote sensing crop classification model with OBIA and ConvLSTM models 11

OB-ConvLSTM: A sequential remote sensing crop classification...

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11th International Conference on Agro-Geoinformatics, Agro-Geoinformatics 2023

作者： Luo, Chen Li, Haiyang Zhang, Jie Wang, Yaojun China Agricultural University College of Information and Electrical Engineering Beijing China

ISBN: (纸本)9798350303513

remote sensing technology has become increasingly important in recent years due to its ability to collect high-resolution images of agricultural fields. One of the most popular methods for crop classification in agricultural fields is object-based image analysis (OBIA). At the same time, the convolutional long short-term memory (ConvLSTM) network has shown great potential in processing spatiotemporal *** this study, we proposed a new model called OB-ConvLSTM (Object-based ConvLSTM) that combines OBIA and ConvLSTM for spatiotemporal crop classification tasks. This model extracts crop spectral information from the spatial dimension of remote sensing images, extracts crop growth information from multiple remote sensing images in the temporal dimension, and synthesizes the spatial and temporal dimension information to improve crop classification accuracy. Compared with traditional crop classification models based on single temporal remote sensing, the model proposed in this study is superior to existing models in classification accuracy and model robustness. The proposed OB-ConvLSTM model has been applied to crop classification tasks in major crop-producing regions, achieving over 93% of the crop species recognition accuracy, with mIoU reaching 83%.The main contribution of this study is to design a temporal remote sensing image semantic segmentation model structure suitable for field crop classification, combining the OBIA method with ConvLSTM and improving the model's performance by optimizing model components such as activation functions and optimizers. Specifically, there are several innovations in the following aspects: First, to facilitate model input, this study uses the SLIC algorithm to segment remote sensing images into uniformly sized superpixel objects and aligns the superpixel objects in the temporal dimension;Subsequently, we used the ConvLSTM model totrain and classify superpixel objects with temporal information, and adopted the Mish activation functi

关键词： remote sensing

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Enhancing Object Detection in Maritime Environments Using Metadata 26th

Enhancing Object Detection in Maritime Environments Using Me...

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26th Iberoamerican Congress on pattern recognition (CIARP)

作者： Fernandes, Diogo Samuel Bispo, Joao Bento, Luis Conde Figueiredo, Monica Univ Porto Fac Engn Porto Portugal Politecn Leiria Leiria Portugal Inst Telecomunicacoes Aveiro Portugal Inst Sistemas & Robot Coimbra Portugal

ISBN: (纸本)9783031492495;9783031492488

Over the years, many solutions have been suggested in order to improve object detection in maritime environments. However, none of these approaches uses flight information, such as altitude, camera angle, time of the day, and atmospheric conditions, to improve detection accuracy and network robustness, even though this information is often available and captured by the UAV. This work aims to develop a network unaffected by image-capturing conditions, such as altitude and angle. To achieve this, metadata was integrated into the neural network, and an adversarial learning training approach was employed. This was built on top of the YOLOv7, which is a state-of-the-art realtime object detector. To evaluate the effectiveness of this methodology, comprehensive experiments and analyses were conducted. Findings reveal that the improvements achieved by this approach are minimal when trying to create networks that generalize more across these specific domains. The YOLOv7 mosaic augmentation was identified as one potential responsible for this minimal impact because it also enhances the model's ability to become invariant to these image-capturing conditions. Another potential cause is the fact that the domains considered (altitude and angle) are not orthogonal with respect to their impact on captured images. Further experiments should be conducted using datasets that offer more diverse metadata, such as adverse weather and sea conditions, which may be more representative of real maritime surveillance conditions. The source code of this work is publicly available at https://git ***/ipleiria-robotics/maritime-metadata-adaptation.

关键词： Computer Vision remote sensing Maritime Surveillance Domain Adaptation Metadata

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Analysis of high-level dance movements under deep learning and internet of things

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JOURNAL OF SUPERCOMPUTING 2022年第12期78卷 14294-14316页

作者： Wang, Shan Tong, Shusheng Guangzhou Univ Coll Mus & Dance Guangzhou 510006 Peoples R China

The purpose is to improve the design effect of high-level dance movements and help dancers to better master these movements. The body changes with advanced dance movements based on deep learning (DL) algorithm and Internet of Things (IoT) technology are analyzed to promote the practical application of biological image visualization technology. Firstly, DL is applied for image recognition, and secondly, the technical architecture of IoT technology is constructed. Finally, DL and IoT-edge computing (IoT-EC) are employed to establish the network structure of the dance generation model. The experimental results indicate that IoT-EC based on DL significantly enhances the efficiency of resource allocation and effectively reduces the server processing time. For 200 tasks in the workspace, deep reinforcement learning can be optimized in only 8 s. When there are 800 tasks in some workspaces, the edge server takes 21 s to optimize deep reinforcement learning (DRL). Besides, this scheme can control the energy consumption of the server in the calculation process while dramatically reducing the average waiting time. The application of these technologies in the dance movement has extensively promoted the progress and development of the dance industry. The present work provides references DL in image recognition and remote sensing image classification.

关键词： Deep learning Internet of Things Biological image visualization technology Dance movements

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A Study on Deep Learning-Based image Target Identification Techniques 5th

A Study on Deep Learning-Based Image Target Identification T...

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5th International Conference on 3D Imaging Technologies—Multidimensional Signal processing and Deep Learning, 3DIT-MSP and DL 2023

作者： Wang, Yining College of Electronic Information and Optical Engineering Taiyuan University of Technology Shanxi Taiyuan China

ISBN: (纸本)9789819751808

This study aims to explore deep learning-based image target recognition methods to improve the performance of target detection and classification in the field of computer vision. The experiments use satellite-acquired remote sensing images of moving objects, including four target categories such as buses, cars, bicycles and ships. In model training, we performed parameter optimisation by stochastic gradient descent algorithm and set the training period to 12. To validate the model’s effectiveness, we compare the improved R-CNN (Region-based Convolutional Neural Network), traditional CNN (Convolutional Neural Network) and Faster-RCNN (Faster Region-based Convolutional Neural Network) models, and generate confusion matrices to evaluate their performance. The experimental results show that the improved model exhibits excellent performance in recognition of different types of target objects, especially for larger targets. This research provides reliable experimental support for the application of deep learning in the field of image target recognition and is expected to promote further development and innovation in this field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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Deep Residual Network with Multi-image Attention for Imputing Under Clouds in Satellite imagery 26

Deep Residual Network with Multi-Image Attention for Imputin...

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

作者： Yang, Xian Zhao, Yifan Vatsavai, Ranga Raju North Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA BReLL Lirio Knoxville TN USA North Carolina State Univ CGA CSC Raleigh NC 27695 USA

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Earth observations from remote sensing imagery play an important role in many environmental applications ranging from natural resource (e.g., crops, forests) monitoring to man-made object (e.g., builds, factories) recognition. Most widely used optical remote sensing data however is often contaminated by clouds making it hard to identify the objects underneath. Fortunately, with the recent advances and increased operational satellites, the spatial and temporal density of image collections have significantly increased. In this paper, we present a novel deep learning-based imputation technique for inferring spectral values under the clouds using nearby cloud-free satellite image observations. The proposed deep learning architecture, extended contextual attention (ECA), exploits similar properties from the cloud-free areas to tackle clouds of different sizes occurring at arbitrary locations in the image. A contextual attention mechanism is incorporated to utilize the useful cloud-free information from multiple images. To maximize the imputation performance of the model on the cloudy patches instead of the entire image, a two-phase custom loss function is deployed to guide the model. To study the performance of our model, we trained our model on a benchmark Sentinel-2 dataset by superimposing real-world cloud patterns. Extensive experiments and comparisons against the state-of-the-art methods using pixel-wise and structural metrics show the improved performance of our model. Our experiments demonstrated that the ECA method is consistently better than all other methods, it is 28.4% better on MSE and 31.7% better on cloudy MSE as compared to the state-of-the-art EDSR network.

关键词： Earth Deep learning Cloud computing Satellites Clouds Benchmark testing Data models

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remote sensing image Registration Via Feature Fusion and Enhanced Matching 6

Remote Sensing Image Registration Via Feature Fusion and Enh...

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6th International Conference on Electronic Information Technology and Computer Engineering, EITCE 2022

作者： Li, Xianjing Chen, Ying Zhang, Wencheng Wang, Jiahao Wang, Wei School of Computer Science and Information Engineering Shanghai Institute of Technology Shanghai China

ISBN: (纸本)9781450397148

image registration is a basic problem in image analysis and image processing. image registration has important applications in aerial image fusion, pattern recognition, three-dimensional reconstruction and other fields. Aiming at the problem of low registration accuracy and mismatching in remote sensing image registration, this paper proposes to use the Involution kernel to improve the ResNext network in the feature extraction stage and combines the SPANet attention mechanism with an improved ResNext network to improve the feature extraction ability of the network. In the feature matching stage, an enhanced matching method is proposed, which uses cross-correlation and nearest neighbor to second nearest neighbor ratio to filter out mismatched points to cope with complex images and background interference. The experimental results show that the proposed algorithm can achieve superior results in a variety of indexes compared with other algorithms, which proves that the proposed algorithm is effective. © 2022 Association for Computing Machinery.

关键词： remote sensing

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Parallel Enhanced Network for image Compressed sensing

Parallel Enhanced Network for Image Compressed Sensing

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2022 International Conference on image, Signal processing, and pattern recognition, ISPP 2022

作者： Ma, Wenna Wu, Xiangjun College of Software Henan University Kaifeng475004 China

ISBN: (纸本)9781510654846

Despite the remarkable progress has made in deep compressed sensing (DCS), how to improve the reconstruction quality is still a major challenge. The existing DCS model generally still has some issues, especially in recovering details. In this paper, a new parallel enhanced network (PENet) is proposed for image compressed sensing. PENet is designed as a sampling network and a parallel network, which contains a basic network and an enhanced network. The basic network is designed to provide the initial reconstructed image. The enhanced network is trained to progressively acquire module details through the connections with each block of the basic network in stages. The final reconstructed image is the cumulative results between the parallel network. Experimental result shows that PENet has a high reconstruction quality and comparable running time complexity with existing advanced DCS methods. © 2022 SPIE.

关键词： Compressed sensing

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基于伪全局Swin Transformer的遥感图像识别算法

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模式识别与人工智能 2023年第9期36卷 818-831页

作者：王科平左鑫浩杨艺费树岷河南理工大学电气工程与自动化学院焦作454003 河南理工大学河南省智能装备直驱技术与控制国际联合实验室焦作454003 东南大学自动化学院南京210096

如何在多目标并列的情况下,确定符合人类思维习惯的核心目标是遥感图像识别的关键之一.因此,在全局视野下,为各目标分配符合人类视觉习惯的注意力,是甄选核心目标的有效途径之一.文中结合Transformer提取全局特征的思想和Swin Transfor... 详细信息

如何在多目标并列的情况下,确定符合人类思维习惯的核心目标是遥感图像识别的关键之一.因此,在全局视野下,为各目标分配符合人类视觉习惯的注意力,是甄选核心目标的有效途径之一.文中结合Transformer提取全局特征的思想和Swin Transformer对图像栅格化处理可降低计算量的优点,提出基于伪全局Swin Transformer的遥感图像识别算法.构建伪全局Swin Transformer模块,将遥感图像栅格化后的各局部信息聚合为一个特征值,替代以像素为基础的全局信息,以较小计算量为代价,获取全局特征,有效提升模型对所有目标的感知能力.同时,通过以可变形卷积为基础的感受野自适应缩放模块,使感受野向核心目标偏移,提高网络对核心目标信息的关注,从而实现对遥感图像的精确识别.在RSSCN7、AID和OPTIMAL-31遥感图像数据集上的实验表明,文中算法取得较高的识别精度和参数识别效率.

关键词：遥感图像识别 Transformer Swin Transformer 核心目标

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