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4th International Workshop on Intelligent Information Technologies and Systems of Information Security, IntellTSIS 2023

作者： Vasilyeva, Irina Lukin, Vladimir Kharchenko, Vyacheslav Nereta, Andriy National Aerospace University 17 Chkalova Street Kharkiv61070 Ukraine Department of Environmental Protection and Nature Management of the Kharkiv Regional State Administration 5 Svobody Square Kharkiv61022 Ukraine

The reliability of remote sensing data classification is significantly affected by the quality of images and the efficiency of algorithms and methods used at all stages of their processing. At the same time, the complementarity of satellite data and the results of aerial photography from UAVs opens up wide opportunities for their joint use in order to improve the accuracy of decision-making about the observed class of objects. We discuss one of the options for the joint use of UAVs and satellite data for the tasks of integrated remote aerospace monitoring of natural and anthropogenic objects. Initially, regions of interest are identified on satellite images, where search objects are most likely to be located. To identify objects with known stochastic properties under conditions of significant a priori uncertainty, it is recommended to use a probabilistic filtering method that allows segmenting an original image into two classes: the "object of interest" and a generalized class that includes all other classes that differ from the searched object by characteristic features. Further, UAV data are involved in the analysis, which can provide reliable information about the presence (or absence) of a given object in the detected search area. This information is used to correct the results of satellite image recognition. The results of segmentation of satellite images (Sentinel-2) and classification of test aerial photographs by the maximum likelihood method are presented. It is shown that UAV data can be useful for analysing or calibrating satellite data, which is especially important for classification procedures based on nonparametric supervised learning methods. © 2023 Copyright for this paper by its authors.

关键词： image classification

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UAV Visual Localization Technology Based on Heterogenous remote sensing image Matching 22

UAV Visual Localization Technology Based on Heterogenous Rem...

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Proceedings of the 2022 5th International Conference on Artificial Intelligence and pattern recognition

作者： Haoyang Tang Jiakun Shi Xin Miao Ruichen Wu Dongfang Yang Xi'an University of Posts and Telecommunications China The Rocket Force University of Engineering China

ISBN: (纸本)9781450396899

At present, the positioning function of intelligent UAVs mainly uses GPS technology, and GPS signals are susceptible to environmental and electromagnetic interference factors. In this paper, we combine remote sensing image processing with image matching algorithms to propose a GPS-independent visual localization technique for UAVs. First, the VGG16 network is used as the feature extraction backbone network, and the backbone network is designed and optimized for the characteristics of heterogenous remote sensing images. Secondly, a feature point screening and matching strategy is constructed, by which common feature points between heterogeneous remote sensing images can be screened and used for feature matching. Finally, the remote sensing image containing geographic location information and the UAV aerial image are fed into the network for feature extraction and matching, and the transformation matrix between the aligned images is calculated by the successfully matched feature points, and the transformation matrix is used to complete the mapping from the aerial image to the satellite image, and finally the geographic location information of each pixel can be read from the mapped image to complete the localization.

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Monitoring the Work Cycles of Earthmoving Excavators in Earthmoving Projects Using UAV remote sensing

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remote sensing 2021年第19期13卷 3853-3853页

作者： Wu, Yiguang Wang, Meizhen Liu, Xuejun Wang, Ziran Ma, Tianwu Lu, Zhimin Liu, Dan Xie, Yujia Li, Xiuquan Wang, Xing Nanjing Normal Univ Key Lab Virtual Geog Environm Minist Educ Nanjing 210023 Peoples R China Jiangsu Ctr Collaborat Innovat Geog Informat Reso Nanjing 210023 Peoples R China Nanjing Normal Univ Sch Geog Nanjing 210023 Peoples R China Nanjing Normal Univ Sch Informat Engn Taizhou Coll Taizhou 225300 Peoples R China Beijing Innovat Ctr Mobil Intelligent Co Ltd Beijing 100163 Peoples R China East China Univ Technol Fac Geomat Nanchang 330013 Jiangxi Peoples R China Nanjing Univ Finance & Econ Coll Informat Engn Nanjing 210023 Peoples R China

Monitoring the work cycles of earthmoving excavators is an important aspect of construction productivity assessment. Currently, the most advanced method for the recognition of work cycles is the "Stretching-Bending" Sequential pattern (SBSP), which is based on fixed-carrier video monitoring (FC-SBSP). However, the application of this method presupposes the availability of preconstructed installation carriers to act as a surveillance camera as well as installed and commissioned surveillance systems that work in tandem with them. Obviously, this method is difficult to apply to projects with no conditions for a monitoring camera installation or which have a short construction time. This highlights the potential application of Unmanned Aerial Vehicle (UAV) remote sensing, which is flexible and mobile. Unfortunately, few studies have been conducted on the application of UAV remote sensing for the work cycle monitoring of earthmoving excavators. This research is necessary because the use of UAV remote sensing for monitoring the work cycles of earthmoving excavators can improve construction productivity and save time and costs, especially in post-disaster reconstruction projects involving harsh construction environments, and emergency projects with short construction periods. In addition, the challenges posed by UAV shaking may have to be taken into account when using the SBSP for UAV remote sensing. To this end, this study used application experiments in which stabilization processing of UAV video data was performed for UAV shaking. The application experimental results show that the work cycle performance of UAV remote-sensing-based SBSP (UAV-SBSP) for UAV video data was 2.45% and 5.36% lower in terms of precision and recall, respectively, without stabilization processing than after stabilization processing. Comparative experiments were also designed to investigate the applicability of the SBSP oriented toward UAV remote sensing. Comparative experimental results show that

关键词： UAV remote sensing earthmoving project earthmoving excavator work cycles "Stretching-Bending" Sequential pattern (SBSP) fixed-carrier video monitoring UAV shaking Intersection Over Union (IOU) computer vision deep learning

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Application of Band Combination in Landslide Identification of remote sensing images Driven by Deep Learning

Application of Band Combination in Landslide Identification ...

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image processing, Computer Vision and Machine Learning (ICICML), International Conference on

作者： Tianyu Yang Weijie Xu Yu Hua College of Computer Science and Cyber Security (Oxford Brookes College) Chengdu University of Technology Chengdu China

With the frequent occurrence of geological disasters, landslide identification has become an important research problem. In recent years, with the application and research of deep learning in the fields of computer vision and remote sensing image analysis, deep learning has become a popular method for landslide identification research because of its excellent feature extraction and pattern recognition capabilities. In this paper, we add new bands reflecting vegetation and water of landslide factors to the original bands of remote sensing image samples by linear combination of bands, and conduct two sets of comparison experiments on Unet and Swin-Unet to verify that adding band features can help improve landslide identification accuracy. The experimental results show that the experimental group with additional band features has a numerical improvement in landslide identification accuracy, with F1-score as the evaluation index, the Unet experimental group has a 2.29% improvement and the Swin-Unet experimental group has a 1.78% improvement. The results of this paper have implications for the subsequent application of bands combination of landslide factor features in remote sensing landslide identification driven by deep learning methods.

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Detecting Out-Of-Distribution Earth Observation images with Diffusion Models

Detecting Out-Of-Distribution Earth Observation Images with ...

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IEEE Computer Society Conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Georges Le Bellier Nicolas Audebert Cnam CEDRIC EA4629 Paris France Univ. Gustave Eiffel ENSG IGN LASTIG Saint-Mandé France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Earth Observation imagery can capture rare and unusual events, such as disasters and major landscape changes, whose visual appearance contrasts with the usual observations. Deep models trained on common remote sensing data will output drastically different features for these out-of-distribution samples, compared to those closer to their training dataset. Detecting them could therefore help anticipate changes in the observations, either geographical or environmental. In this work, we show that the reconstruction error of diffusion models can effectively serve as unsupervised out-of-distribution detectors for remote sensing images., using as a plausibility score. Moreover, we introduce ODEED, a novel reconstruction-based scorer using the probability-flow ODE of diffusion models. We validate it experimentally on SpaceNet 8 with various scenarios, such as classical OOD detection with geographical shift and near-OOD setups: pre/post-flood and non-flooded/flooded image recognition. We show that our ODEED scorer significantly outperforms other diffusion-based and discriminative baselines on the more challenging near-OOD scenarios of flood image detection, where OOD images are close to the distribution tail. We aim to pave the way towards better use of generative models for anomaly detection in remote sensing.

关键词： Training Visualization Tail Diffusion models Data models Satellite images pattern recognition

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Diffusion posterior sampling for remote sensing image fusion

Diffusion posterior sampling for remote sensing image fusion

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7th International Conference on Vision, image and Signal processing (ICVISP 2023)

作者： C. Zhang Z. Wang J. Han College of science National University of Defense Technology Changsha People's Republic of China

remote sensing image fusion, i.e., fusing remote sensing images from different sensors or different time into a comprehensive image, can integrate image information for all kinds of image tasks, such as object detection, pattern recognition, and so on. In this paper, we focus on the image fusion of optical image and synthetic aperture radar (SAR) image, where the traditional methods, such as sparse representation and image decomposition, usually fail to improve the image quality and enhance object features' level. Inspired by the powerful generation model named diffusion denoising probabilistic model (DDPM), we propose a novel method based on diffusion posterior sampling for image fusion of optical image and SAR image. Starting from the variational model of image fusion and the total variation constraint, we approximate the posterior sampling process of image fusion by a closed-form analytic solution. After that, DDPM can be used to generate the fused image with high image quality and enhanced object features. The feasibility and the superiority of the proposed method is validated in numerical experiments.

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Attention Modules Improve image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study

Attention Modules Improve Image-Level Anomaly Detection for ...

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IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)

作者： Vieira E Silva, Andre Luiz Simties, Francisco Kowerko, Danny Schlosser, Tobias Battisti, Felipe Teichrieb, Veronica Univ Fed Pernambuco Ctr Informat Voxar Labs Recife PE Brazil Tech Univ Chemnitz Jr Professorship Media Comp Chemnitz Germany Univ Fed Rural Pernambuco Visual Comp Lab DC Recife PE Brazil

ISBN: (纸本)9798350318920;9798350318937

Within (semi-)automated visual industrial inspection, learning-based approaches for assessing visual defects, including deep neural networks, enable the processing of otherwise small defect patterns in pixel size on high-resolution imagery. The emergence of these often rarely occurring defect patterns explains the general need for labeled data corpora. To alleviate this issue and advance the current state of the art in unsupervised visual inspection, this work proposes a DifferNet-based solution enhanced with attention modules: AttentDifferNet. It improves image-level detection and classification capabilities on three visual anomaly detection datasets for industrial inspection: InsPLAD-fault, MVTec AD, and Semiconductor Wafer. In comparison to the state of the art, AttentDifferNet achieves improved results, which are, in turn, highlighted throughout our quali-quantitative study. Our quantitative evaluation shows an average improvement compared to DifferNet - of 1.77 +/- 0.25 percentage points in overall AUROC considering all three datasets, reaching SOTA results in InsPLAD-fault, an industrial inspection in-the-wild dataset. As our variants to AttentDifferNet show great prospects in the context of currently investigated approaches, a baseline is formulated, emphasizing the importance of attention for industrial anomaly detection both in the wild and in controlled environments.

关键词： Algorithms Algorithms and algorithms Applications formulations image recognition and understanding Machine learning architectures remote sensing

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Dual Stream Fusion Network for Multi-spectral High Resolution remote sensing image Segmentation 4th

Dual Stream Fusion Network for Multi-spectral High Resolutio...

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4th Chinese Conference on pattern recognition and Computer Vision (PRCV)

作者： Cao, Yong Shi, Yiwen Liu, Yiwei Huo, Chunlei Xiang, Shiming Pan, Chunhong Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China Chinese Acad Sci Inst Automat Natl Lab Pattern Recognit Beijing 100190 Peoples R China Beijing City Univ Intelligent Elect Mfg Res Ctr Beijing 100191 Peoples R China Beijing Univ Civil Engn & Architecture Beijing 102627 Peoples R China

ISBN: (纸本)9783030880071;9783030880064

Semantic segmentation is in-demand in High Resolution remote sensing (HRRS) image processing. Unlike natural images, HRRS images usually provide channels such as Near Infrared (NIR) in addition to RGB channels. However, in order to make use of the pre-trained model, the current semantic segmentation methods in remote sensing field usually only use the RGB channel and discard the information of other channels. In this paper, to make full use of the HRRS image information, a dual-stream fusion network is proposed to fuse the information of different channel combinations through a Feature Pyramid Network (FPN), then a Stage Pyramid Pooling (SPP) module is used to integrate the features of different scales and produce the final segmentation results. Experiments on the RSCUP competition dataset show that the proposed approach can effectively improve the segmentation performance.

关键词： Semantic segmentation remote sensing Stream fusion

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FocalSR: Revisiting image super-resolution transformers with fourier-transform cross attention layers for remote sensing image enhancement

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Geomatica 2025年第1期77卷

作者： Ou, Botong Shao, Gang Yang, Baijian Fei, Songlin Computer and Information Technology Purdue University West LafayetteIN47907 United States Libraries and School of Information Studies Purdue University West LafayetteIN47907 United States Forestry and Natural Resources Purdue University West LafayetteIN47907 United States

Transformer architecture has attained noteworthy performance achievements in recent image super-resolution research. However, current transformer-based methods still expose limitations in fully harnessing domain-specific information within images, particularly when applied to broader-scale remote sensing images that contain diverse landscape objects on one scene. remote sensing images have relatively lower resolution compared to the common super-resolution training dataset and each landscape object covers a small area on the image. These natures of remote sensing images significantly reduced the attention pixels for image restoration in existing transformer-based methods. To address this challenge and enhance domain-specific multi-object image reconstruction, we introduce FocalSR, a Transformer model featuring FOurier-transform Cross Attention Layers for Super-Resolution. Drawing inspiration from state-of-the-art Transformer models like Hybrid Attention Transformer (HAT), FocalSR incorporates channel-focused and window-centric self-attention mechanisms. By integrating Fast Fourier Convolution into the cross-attention layer, FocalSR extends its capacity to capture image-wide information and intricate details in low-resolution images. Through unified task pretraining during model development, we validate the efficacy of these enhancements through extensive testing, resulting in substantial performance improvements. Notably, our experiments showcase FocalSR's superior performance in remote sensing datasets, demonstrating a notable 1 dB enhancement in the PSNR metric compared to other state-of-the-art methods. Additionally, significant improvements are observed in challenging scenarios such as pattern restoration and vegetation detail preservation, underscoring the transformative potential of FocalSR in advancing image processing and domain-specific vision tasks. © 2024 The Authors

关键词： Restoration

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Leveraging Segment-Anything model for automated zero-shot road width extraction from aerial imagery

Leveraging Segment-Anything model for automated zero-shot ro...

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2023 International Conference on Digital image Computing: Techniques and Applications, DICTA 2023

作者： Xu, Nan Nice, Kerry Seneviratne, Sachith Stevenson, Mark Faculty of Engineering and It Department of Infrastructure Engineering Australia Melbourne School of Design Transport Health and Urban Systems Research Lab Australia The University of Melbourne Optimization and Pattern Recognition Group Faculty of Engineering and It ParkvilleVIC Australia

ISBN: (纸本)9798350382204

Segment-Anything model (SAM) is a foundation segmentation model published in April 2023. Trained on an unprecedented 11 million annotated images, the model can generate segmented masks bearing clear-cut contours by integrating user-provided prompts. It is zero-shot transferable, requiring no task-specific training. Nevertheless, its applicability for geographic vision tasks has not been fully evaluated. There is no automated prompt-feeding method incorporating with SAM that can work efficiently for purposeful batch processing as well. To fill these gaps, we developed a process that can be executed automatically from visual-prompts extraction to road width measurement, utilizing OpenStreetMap (OSM) and SAM. By examining the quality of segmentation in various image contexts, we evaluated the capacity and limitations of SAM working on aerial imagery. Through comparing measured widths to VicRoads records, we validated the specially designed width-measuring algorithm for high precision and accuracy. After this process, prompt-indicated zero-shot approach in solving basic geographic vision tasks is to be shaped synchronously on both theory and application ends. © 2023 IEEE.

关键词： remote sensing

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