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Moving Vehicle Detection for remote sensing Video Surveillance With Nonstationary Satellite Platform

IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2022年第9期44卷 5185-5198页

作者： Zhang, Junpeng Jia, Xiuping Hu, Jiankun Tan, Kun Univ New South Wales Sch Engn & Informat Technol SEIT Canberra NSW 2600 Australia East China Normal Univ Key Lab Geog Informat Sci Shanghai 200241 Peoples R China

With satellite platforms gazing at a target territory, the captured satellite videos exhibit local misalignment and local intensity variation on some stationary objects that can be mistakenly extracted as moving objects and increase false alarm rates. Typical approaches for mitigating the effect of moving cameras in moving object detection (MOD) follow domain transformation technique, where the misalignment between consecutive frames is restricted to the image planar. However, such technique cannot properly handle satellite videos, as the local misalignment on them is caused by the varying projections from the 3D objects on the Earth's surface to 2D image planar. In order to suppress the effect of moving satellite platform in MOD, we propose a Moving-Confidence-Assisted Matrix Decomposition (MCMD) model, where foreground regularization is designed to promote real moving objects and ignore system movements with the assistance of a moving-confidence score estimated from dense optical flows. For solving the convex optimization problem in MCMD, both batch processing and online solutions are developed in this study, by adopting the alternating direction method and the stochastic optimization strategy, respectively. Experimental results on the videos captured by SkySat and Jilin-1 show that MCMD outperforms the state-of-the-art techniques with improved precision by suppressing effect of nonstationary satellite platforms.

关键词： Satellites Matrix decomposition Optimization Optical sensors Cameras Sparse matrices remote sensing Moving object detection remote sensing video surveillance satellite video low rank matrix decomposition moving satellite platform

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DS-Swin Transformer HRNet for remote sensing images 4

DS-Swin Transformer HRNet for Remote sensing images

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4th International Conference on Computer Vision and pattern Analysis, ICCPA 2024

作者： Wu, Guinan Wu, Qinghong University of Science and Technology Liaoning Liaoning Province Anshan China

ISBN: (纸本)9781510682528

With the continuous development of remote sensing imagery in deep learning, this paper proposes a self-attention model called Dual-Stream Swin Transformer to address the computational and memory requirements issues traditional Transformers face when dealing with high-resolution images. Specifically, 1) The Dual-Stream Swin Transformer in this paper adopts an innovative approach by decomposing the traditional Transformer encoder layer into smaller building blocks and introducing a shifted windows mechanism to construct self-attention. 2) Traditional Transformer models require significant computational and storage resources when processing high-resolution images because they perform self-attention calculations on the entire global image. However, the Swin Transformer significantly reduces the computational and memory requirements by segmenting the image into multiple spatially overlapping windows and performing self-attention calculations within each window. 3) Furthermore, the introduced shifted windows mechanism ensures that the attention of each window is only related to its adjacent windows, further reducing the computational complexity. This decomposition and window mechanism combination makes the Swin Transformer ideal for handling high-resolution visual inputs. It achieves high precision while offering higher computational efficiency and lower memory consumption. This makes the Swin Transformer perform excellently in image classification, object detection, and semantic segmentation tasks. We conducted comparative experiments between this model and other classical network models of the same type. The Dual-Stream Swin Transformer in this paper effectively addresses traditional Trans-formers' computational and memory challenges when handling high-resolution images through innovative decomposition and window mechanisms, providing a new solution for efficiently processing large-scale visual data. © 2024 SPIE.

关键词： Semantic Segmentation

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A novel sarnede method for real-time ship detection from synthetic aperture radar image

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年第12期81卷 16921-16944页

作者： Raj, Anil J. Idicula, Sumam Mary Paul, Binu Cochin Univ Sci & Technol Dept Comp Sci Kochi Kerala India Cochin Univ Sci & Technol Sch Engn Div Elect Engn Kochi Kerala India

Deep learning-based ship detection from SAR data is one of the challenging problems in the remote sensing area. Also, SAR ship detection is precise object detection and pattern recognition task under the computer vision area. The main problems are false detection, primarily due to speckle presence and multi-scale SAR image availability. We propose a novel real-time system with a preprocessing technique exclusively for SAR ship detection to address this problem. The proposed SarNeDe preprocessing stage is specially designed using image processing techniques and lee filter to reduce the false prediction and improve the SAR image quality, which increases the detection accuracy because the lee filter alone could increase missed detections. The SarNeDe image is generated from raw SAR image and is given to a novel multi-scale lightweight deep learning model to predict all ships' positions. The proposed model has a feature merging & boosting network and three detection parts for detecting big, medium, & small ships. We experimented on the public SAR ship detection dataset (SSDD) and Dataset of Ship Detection for Deep Learning under Complex Backgrounds (SDCD) to validate the proposed method's feasibility. The experimental results indicated that our proposed method's ship detection accuracy is superior to other state-of-the-art ship detectors with reduced false detections.

关键词： Ship detection SAR Deep learning Convolution neural network Marine target detection

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Side-slither Radiometric Calibration Mode Design and In-orbit Verification of Ziyuan-1(02D) Satellite Hyperspectral imager

Side-slither Radiometric Calibration Mode Design and In-orbi...

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

作者： Liang, Deyin Yu, Jing Han, Bo Zhu, Haijian Institute of Remote Sensing Satellite CAST Beijing100094 China Shanghai Institute of Technical physics Shanghai200083 China

ISBN: (纸本)9781510654846

The radiometric calibration accuracy of a hyperspectral imager is a key link in its quantitative application. The side-slither radiometric calibration can realize the high-frequency and full field of view relative radiometric correction of the hyperspectral imager, which lays the foundation for the subsequent quantitative application of hyperspectral data. In this paper, a hyperspectral imager relative radiometric calibration method and data processing method based on satellite platform 90° yaw maneuver is proposed. A verification of this method is carried out based on the in-orbit data of Ziyuan-1(02D) satellite. The results show that the proposed method has good performance for relative radiometric calibration. The relative radiometric calibration accuracy of all the sensor pixels can reach 1%, and the fringe noise in the original image is well removed. © 2022 SPIE.

关键词： Satellites

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Controlled Multi-modal image Generation for Plant Growth Modeling 26

Controlled Multi-modal Image Generation for Plant Growth Mod...

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

作者： Miranda, Miro Drees, Lukas Roscher, Ribana Univ Bonn IGG Remote Sensing Niebuhrstr 1a D-53113 Bonn Germany Tech Univ Munich Data Sci Earth Observat Lise Meitner Str 9 D-85521 Ottobrunn Germany German Res Ctr Artificial Intelligence DFKI Trippstadter Str 122 D-67663 Kaiserslautern Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Predicting plant development is an important task in precision farming and an essential metric for decision-making by researchers and farmers. In this work, we propose a novel generative modeling technique for plant growth prediction based on conditional generative adversarial networks. We formulate plant growth as an image-to-image translation task and predict the appearance of a plant growth stage as a function of its previous stage. We take into account that plant growth is inherently multi-modal, depending on numerous and highly variable environmental factors, and thus a single input belongs to a distribution of potential outputs. We encode the ambiguity in an interpretable and low-dimensional latent vector space representing the various factors of variation that are influencing plant growth. We use a novel encoder-based data fusion technique and combine information contained in remote sensing imagery of different cropping systems with data containing the factors of variation to adequately model plant growth. This offers several advantages over existing methods: (1) we show that we can model a distribution of potential appearances and simultaneously outperform existing methods in providing more realistic predictions, (2) the complexity of plant growth is more adequately captured, as various factors influencing plant growth can be included, (3) predictions are controllable by being conditioned by an interpretable latent vector representing the factors of variation along with an input image of a previous growth stage.

关键词： Measurement image synthesis Ecosystems Predictive models Generative adversarial networks pattern recognition Task analysis

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CA-YOLOX: Deep Learning-Guided Road Intersection Location From High-Resolution remote sensing images

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INTERNATIONAL JOURNAL OF pattern recognition AND ARTIFICIAL INTELLIGENCE 2023年第14期37卷 2351017-2351017页

作者： Li, Chengfan Zhang, Zixuan Liu, Lan Wang, Shengnan Zhao, Junjuan Liu, Xuefeng Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Wuhan Univ Key Lab Natl Geog Census & Monitoring Minist Nat Resources Wuhan 430079 Peoples R China East China Univ Technol Key Lab Digital Land & Resources Jiangxi Prov Nanchang 330013 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shangda 99 Shanghai 200444 Peoples R China Shanghai Univ Engn Sci Sch Elect & Elect Engn Longteng 333 Shanghai 201620 Peoples R China Shanghai Univ Sch Commun & Informat Engn Shangda 99 Shanghai 200444 Peoples R China

The location of road intersection from high resolution remote sensing (HRRS) images can be automatically obtained by deep learning. This has become one of the current data sources in urban smart transportation. However, limited by the small size, diverse types, complex distribution, and missing sample labels of road intersections in actual scenarios, it is difficult to accurately represent key features of road intersection by deep neural network (DNN) model. A new coordinate attention (CA) module-YOLOX (CA-YOLOX) method for accurately locating road intersections from HRRS images is presented. First, the spatial pyramid pooling (SPP) module is introduced into the backbone convolution network between the Darknet-53' last feature layer and feature pyramid networks (FPN) structure. Second, the CA module is embedded into the feature fusion structure in FPN to focus more on the spatial shape distribution and texture features of road intersections. Third, we use focal loss to replace the traditional binary cross entropy (BCE) loss in the confidence loss to improve the iteration speed of the CA-YOLOX network. Finally, an extensive empirical experiment on Potsdam, IKONOS datasets, and ablation study is then implemented and tested. The results show that the presented CA-YOLOX method can promote the location accuracy of road intersection from HRRS images compared to the traditional You only look once (YOLO) model.

关键词： Road intersection object location deep learning coordinate attention (CA) high-resolution remote sensing (HRRS) image

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Detecting Adversarial Examples for Hyperspectral image Classification Via Mutation Testing 13

Detecting Adversarial Examples for Hyperspectral Image Class...

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13th Workshop on Hyperspectral Imaging and Signal processing: Evolution in remote sensing, WHISPERS 2023

作者： Hao, Yang Chen, Zhifei Xiao, Liang Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China

ISBN: (纸本)9798350395570

With the widespread application of remote sensing technology in various fields, convolutional neural networks (CNNs) have exhibited remarkable performance in the field of hyperspectral image (HSI) classification. However, CNNs also introduce security concerns when demonstrating their accurate classification capabilities. By adding imperceptible and carefully designed adversarial perturbations to input images, CNN models can produce highly confident incorrect results. These images, which have adversarial perturbations added to them, are known as adversarial samples. In this work, we propose a mutation testing-based method for detecting adversarial examples. In our experiments, we employ different attack algorithms to deceive multiple high-accuracy HSI recognition models trained on multiple HSI datasets, and our method aims to determine whether the generated adversarial examples are normal or adversarial. We evaluate our method on the Indian Pines, Salinas, and University of Pavia datasets. The results demonstrate that our method can effectively detect adversarial examples generated by state-of-the-art attack methods. © 2023 IEEE.

关键词： image classification

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基于特征重组的遥感图像有向目标检测

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光学学报 2024年第6期44卷 318-328页

作者：王友伟郭颖邵香迎王季宇鲍正位南京信息工程大学江苏省大气环境与装备技术协同创新中心江苏南京210044 南京信息工程大学自动化学院江苏南京210044

针对遥感有向目标存在的检测问题,设计了一个基于改进Rotated RPN的网络,设计特征重组机制,通过加权使网络关注有效目标区域。使用新的有向框标注方法,避免在临界角度出现错位等问题。在检测头前端使用极化注意力模块,改善因为分类和回... 详细信息

针对遥感有向目标存在的检测问题,设计了一个基于改进Rotated RPN的网络,设计特征重组机制,通过加权使网络关注有效目标区域。使用新的有向框标注方法,避免在临界角度出现错位等问题。在检测头前端使用极化注意力模块,改善因为分类和回归任务所需特征不一致导致的性能下降问题。实验结果表明,该模型可以提高多类目标的检测精度。相较于基准Rotated RPN,该模型在Dior-R数据集上精度提升4.95%,在HRSC2016数据集上精度提升11.75%。

关键词：遥感有向目标检测深度学习特征重组极化注意力

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An Enhanced Surveillance System Integrating Pedestrian Attribute recognition and Multi-Target Multi-Camera Tracking Technologies 45

An Enhanced Surveillance System Integrating Pedestrian Attri...

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45th Asian Conference on remote sensing, ACRS 2024

作者： Lai, Yi-Cheng Huang, Chih-Yuan Hsu, Yu-Sheng Department of Civil Engineering National Central University Taiwan Center for Space and Remote Sensing Research National Central University Taiwan

Surveillance systems usually connect to multiple cameras for real-time video feeds. Traditionally, finding certain objects from these video feeds is a time-consuming and labor-intensive task, requiring manual monitoring or review by human operators. Such manual processes can lead to inefficiencies, especially when processing large amounts of footage in a short period. With the rapid development of AI-based computer vision, this research tries to enhance surveillance systems by integrating Pedestrian Attribute recognition (PAR) and Multi-Target Multi-Camera Tracking (MTMCT) technologies, creating a more efficient and automated solution. The PAR is to identify distinct appearance characteristics of a targeted person from images, enabling surveillance systems to identify and classify various attributes of pedestrians in a textual format, such as gender, age, hairstyle, types of clothing, accessories, etc. These attributes may also include more detailed features, such as posture and movement patterns, allowing for more precise search capabilities. On the other hand, the MTMCT is for identifying and tracking moving objects across multiple cameras, which requires a series of AI-based processes, including object detection and tracking on images from individual cameras, re-identify objects when moving from one camera to another, etc. This tracking process involves various complexities, including ensuring accurate re-identification of objects when they leave one camera’s view and enter another, which is key to maintaining continuous tracking. By integrating PAR and MTMCT technologies, a surveillance system can quickly locate target individuals from a large amount of image data based on space, time, and appearance information, which is extremely useful for various applications, including crime investigations, searching for missing persons, etc. In addition, as PAR models can extract local and global appearance features, these features can also help improve the re-identificati

关键词： remote sensing

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remote sensing Road Segmentation Based on Feature Extraction Optimization and Skeleton Detection Optimization 7th

Remote Sensing Road Segmentation Based on Feature Extraction...

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7th China High-resolution Earth Observation Conference, CHREOC 2020

作者： Dong, Sijun Zhao, Yu Chen, Zhengchao Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China University of Chinese Acdemy of Sciences Beijing100049 China

ISBN: (纸本)9789811657344

High-resolution remote sensing image segmentation is a mature application in many industrial-level image applications, such as those in the military, civil, and other fields. Scene analysis needs to be automated in high-resolution remote sensing images as much as possible. Nowadays, with the rise of deep learning algorithms, remote sensing image processing algorithms have made tremendous progress. Deep learning algorithms process unlabeled data by learning a certain amount of labeled data. We conducted a specific study on the road target with GF1 data collected in China, and the remote sensing image’s resolution was 2 m (Jin et al. in J Anhui Agri Sci 43:358–362, 2015 [Jin et al. in J Anhui Agri Sci 43:358–362, 2015]). According to the observation of road features in remote sensing images, it still has a large number of small roads that are difficult to distinguish in the 2 m resolution GF1 remote sensing image. Due to the limitations of the downsampling calculation of the fully convolutional neural network, it is easy to lose a lot of information on small roads (Long et al. in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440, 2015 [Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp 3431–3440]). Therefore, we have adjusted the feature extraction and backbone networ. We adopted EfficientNet (Tan and Le in Efficientnet: Rethinking model scaling for convolutional neural networks, 2019 [Tan M, Le QV (2019) Efficientnet: rethinking model scaling for convolutional neural networks. arXiv preprint arXiv:1905.11946]) as the skeleton network of the algorithm, and combined D-linkNet (Zhou et al. in CVPR workshops, pp. 182–186, 2018 [Zhou L, Zhang C, Wu M (2018) D-LinkNet: LinkNet with pretrained encoder and dilated convolution for high resolution satellite imagery road extraction. In: CVPR workshops,

关键词： Feature extraction

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