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IEEE/CVF Conference on Computer Vision and pattern recognition (CVPR)

作者： Li, Zhuohong Lu, Fangxiao Zou, Jiaqi Hu, Lei Zhang, Hongyan Wuhan Univ Wuhan Peoples R China China Univ Geosci Wuhan Peoples R China

ISBN: (纸本)9798350365474

Land-cover mapping is one of the vital applications in Earth observation, aiming at classifying each pixel's land-cover type of remote-sensing images. As natural and human activities change the landscape, the land-cover map needs to be rapidly updated. However, discovering newly appeared land-cover types in existing classification systems is still a non-trivial task hindered by various scales of complex land objects and insufficient labeled data over a wide-span geographic area. In this paper, we propose a generalized few-shot segmentation-based framework, named SegLand, to update novel classes in high-resolution land-cover mapping. Specifically, the proposed framework is designed in three parts: (a) Data pre-processing: the base training set and the few-shot support sets of novel classes are analyzed and augmented;(b) Hybrid segmentation structure: Multiple base learners and a modified Projection onto Orthogonal Prototypes (POP) network are combined to enhance the base-class recognition and to dig novel classes from insufficient labels data;(c) Ultimate fusion: the semantic segmentation results of the base learners and POP network are reasonably fused. The proposed framework has won first place in the leaderboard of the OpenEarthMap Land Cover Mapping Few-Shot Challenge. Experiments demonstrate the superiority of the framework for automatically updating novel land-cover classes with limited labeled data.

关键词： Few-shot Land-cover mapping semantic segmentation

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UHV transmission line monitoring based on satellite remote sensing

UHV transmission line monitoring based on satellite remote s...

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2023 International Conference on image, Signal processing, and pattern recognition, ISPP 2023

作者： Zhang, Yunfeng Zhang, Peng Shen, Jianliang Wei, Wenli Yue, Lingping Zhang, Jun State Grid Huzhou Power Supply Company Zhejiang Huzhou313000 China Center of Mass Entrepreneurship Innovation State Grid Zhejiang Electric Power Co. Ltd Zhejiang Hangzhou310000 China NARI-TECH Nanjing Control Systems Ltd Jiangsu Nanjing210000 China

ISBN: (纸本)9781510666351

UHV transmission lines have problems such as high impedance and low loss. This paper mainly introduces the monitoring method of UHV transmission and distribution line based on satellite remote sensing technology, aiming at avoiding or reducing property losses caused by line problems and maintaining people's normal life. In this paper, the experimental system construction and quantitative analysis methods are mainly used, and the results of two kinds of line detection are compared by comparison. The experimental data shows that in different types of fault lines, the test time of fault is within 0.1s. © 2023 SPIE.

关键词： Electric lines

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Intelligent Bionic Polarization Orientation Method Using Biological Neuron Model for Harsh Conditions

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IEEE TRANSACTIONS ON pattern ANALYSIS AND MACHINE INTELLIGENCE 2025年第2期47卷 789-806页

作者： Shen, Chong Wu, Yicheng Qian, Guanyu Wu, Xindong Cao, Huiliang Wang, Chenguang Tang, Jun Liu, Jun North Univ China Sch Instrument & Elect State Key Lab Dynam Measurement Technol Shanxi Prov Key Lab Quantum Sensing & Precis Measu Taiyuan 030051 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China North Univ China Sch Informat & Commun Engn Taiyuan 030051 Peoples R China

We developed an intelligent innovative orientation method to improve the accuracy of polarization compasses in harsh conditions: weak skylight polarization patterns resulting from unfavorable weather conditions (e.g., haze, sandstorms) or locally destroyed skylight polarization conditions caused by occlusions (e.g., buildings, trees). First, the skylight polarization status was determined with the degree of linear polarization threshold analysis method and a bionic polarization enhancement sensing model was constructed to simulate the enhanced perception mechanism identified in the Syrphidae visual neural pathway, highly efficient in dark or weakly illuminated environments. The bionic model successfully enhanced the information content extracted from weak polarization patterns. Second, polarization pixel interferences, caused by occlusions under locally destroyed skylight polarization conditions, were removed with a convolutional neural network for image segmentation and the sky area of interest was identified. Finally, the incomplete angle of polarization map derived after image segmentation was fitted using our optimized adaptive antisymmetric ring algorithm. On the basis of the strong angle-of-polarization antisymmetry along the solar meridian, information extracted from the sparse and irregular polarization pixels was analyzed to derive a high-accuracy polarization orientation solution. The whole method intelligently realizes pattern analysis and deep learning intelligent processing, efficiently rotates to manage polarization disorientation. The experimental results demonstrated the performance of the proposed method in compensating for reduced orientation accuracy under degraded polarization conditions, its robustness against perturbations, and its beneficial impact on the environmental adaptability of bionic polarization compasses.

关键词： Compass Accuracy Sensors Navigation Meteorology Histograms Clouds Biological system modeling Atmospheric modeling Buildings Adaptive orientation unperturbed navigation biological visual neuron model image segmentation polarization compass

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FFENet: Learning Frequency Features for Low-Light Enhancement

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Liu, Zhenbing Gong, Junming Lu, Haoxiang Pan, Xipeng Lan, Rushi Guilin Univ Elect Technol Sch Comp & Informat Secur Guilin 541004 Peoples R China

The visualization and analysis of images captured under low-light conditions have long been a significant challenge in various fields, including surveillance, astronomy, medical imaging, and remote sensing. This work presents a new low-light image enhancement (LLIE) method called frequency features enhancement network (FFENet). We can observe that the image contains different levels of information at high and low frequencies, so we can achieve more accurate enhancement by processing the high and low frequencies separately according to their different information characteristics. Specifically, we first obtain the high- and low-frequency information of the image through the frequency decomposition (FD) block. Then, we use a contrast stretch subnetwork (CSNet) to realize the enhancement of the content of low frequency and apply a high-frequency enhancement subnetwork (HENet) to complete the detail extraction and noise suppression of high frequency. Finally, in the fusion stage, the network obtains images with normal illumination. Extensive experimental results suggest that our FFENet superiors several state-of-the-art approaches benefiting from its stronger multiscale feature expression and extraction ability. In addition, experiments also indicate that object recognition and other high-level computer applications on low-light images can be promoted owing to our designed FFENet method.

关键词： Lighting image color analysis Visualization Noise image restoration Colored noise Feature extraction Brightness Information filters image enhancement Channel attention mechanism frequency decomposition (FD) low-light image enhancement (LLIE) multiscale representation

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An Edge-Guided Network for Building Footprint Extraction from Optical remote sensing images 12

An Edge-Guided Network for Building Footprint Extraction fro...

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SPIE 12th International Symposium on Multispectral image processing and pattern recognition, MIPPR 2023

作者： Zhou, Yuhang Tan, Yihua National Key Laboratory of Multispectral Information Intelligent Processing Technology School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

ISBN: (数字)9781510675001

ISBN: (纸本)9781510674998

Accurate building footprint extraction from optical remote sensing images remains challenging due to the diverse appearance and complex scenarios. Although recent deep learning-based methods have been shown to greatly improve the accuracy of building footprint extraction, vanilla deep networks still suffer from ambiguous predictions of edge pixels. The building edge contains abundant location and shape information, which is important for downstream applications such as building positioning and area measurement. Therefore, the problem of inaccurate edge prediction needs to be resolved urgently. To this end, we propose a novel edge-guided network (EGNet) that makes ample use of the edge prior in an end-to-end manner. First, an edge extraction module (EEM) is proposed to extract the building edge map. Then, an edge-guidance module (EGM) is designed to utilize the edge map to guide each encoder block in extracting edge-related features. Furthermore, a multi-scale context aggregation module (MCAM) is built to enhance the feature representation by aggregating contextual semantics with different receptive fields. EGNet can effectively mine edge semantics and guide the representation learning of boundaries, achieving 75.21% and 91.16% IoU on the Massachusetts and WHU datasets, respectively. Experimental results demonstrate that the proposed EGNet has a certain superiority in both accuracy and efficiency compared with current state-of-the-art (SOTA) methods. © 2024 SPIE.

关键词： Extraction

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Semantic-Guided Transformer Network for Crop Classification in Hyperspectral images

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JOURNAL OF IMAGING 2025年第2期11卷 37-37页

作者： Pi, Weiqiang Zhang, Tao Wang, Rongyang Ma, Guowei Wang, Yong Du, Jianmin Huzhou Vocat & Tech Coll Coll Intelligent Mfg & Elevator Huzhou 313099 Peoples R China Inner Mongolia Agr Univ Coll Mech & Elect Engn Hohhot 010018 Peoples R China

The hyperspectral remote sensing images of agricultural crops contain rich spectral information, which can provide important details about crop growth status, diseases, and pests. However, existing crop classification methods face several key limitations when processing hyperspectral remote sensing images, primarily in the following aspects. First, the complex background in the images. Various elements in the background may have similar spectral characteristics to the crops, and this spectral similarity makes the classification model susceptible to background interference, thus reducing classification accuracy. Second, the differences in crop scales increase the difficulty of feature extraction. In different image regions, the scale of crops can vary significantly, and traditional classification methods often struggle to effectively capture this information. Additionally, due to the limitations of spectral information, especially under multi-scale variation backgrounds, the extraction of crop information becomes even more challenging, leading to instability in the classification results. To address these issues, a semantic-guided transformer network (SGTN) is proposed, which aims to effectively overcome the limitations of these deep learning methods and improve crop classification accuracy and robustness. First, a multi-scale spatial-spectral information extraction (MSIE) module is designed that effectively handle the variations of crops at different scales in the image, thereby extracting richer and more accurate features, and reducing the impact of scale changes. Second, a semantic-guided attention (SGA) module is proposed, which enhances the model's sensitivity to crop semantic information, further reducing background interference and improving the accuracy of crop area recognition. By combining the MSIE and SGA modules, the SGTN can focus on the semantic features of crops at multiple scales, thus generating more accurate classification results. Finally, a two-st

关键词： hyperspectral image classification transformer deep learning attention mechanism convolutional neural network

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Spatiotemporal pattern Evolution for Vegetation Using remote sensing Technology: A case study of Zhoushan Island, China

Spatiotemporal Pattern Evolution for Vegetation Using Remote...

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2024 International Conference on remote sensing, Mapping, and image processing, RSMIP 2024

作者： Chen, Yankun Chen, Chao Liu, Zhisong School of Information Engineering Zhejiang Ocean University Zhoushan316022 China School of Geography Science and Geomatics Engineering Suzhou University of Science and Technology Suzhou215009 China Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province Zhoushan316022 China

ISBN: (纸本)9781510680012

For ecological environment protection research and regional sustainable development, vegetation, as a crucial land cover type, necessitates long-term, large-scale, and highly accurate monitoring. This paper formulates an enduring vegetation monitoring approach using Landsat satellite remote sensing imagery on the Google Earth Engine (GEE) cloud platform. By computing the Normalized Difference Vegetation Index (NDVI), we formulate a method for prolonged remote sensing monitoring of vegetation, unveiling its temporal and spatial pattern evolution. Firstly, based on Landsat remote satellite imagery and GEE, we calculated the NDVI. By leveraging the Normalized Difference Water Index (NDWI), we removed inland water bodies and outliers within the study area, enabling the precise generation of spatiotemporal NDVI distribution maps. Subsequently, the NDVI was analyzed for time trends in conjunction with grade transfer matrices. Finally, combined with Moran's I, high/low clusters, clustering and outlier, cold/hot spots, etc., the NDVI spatial pattern characteristics were analyzed. Using the Zhoushan Island in China as an example, the results indicated that: (1) The most significant changes were observed in the low NDVI area (0-0.2), which expanded from 28.84 km2 to 67.29 km2. (2) Concentrated mainly in the inland areas are the NDVI High-High clusters and the significant hot spots, while the coastal areas primarily feature NDVI Low-Low clusters and significant cold spots. © 2024 SPIE.

关键词： Vegetation

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Evaluating and testing neural-network algorithm capabilities for automating image data analysis for remote sensing of the Earth

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JOURNAL OF OPTICAL TECHNOLOGY 2022年第10期89卷 607-614页

作者： Mingalev, A. V. Belov, A. V. Gabdullin, I. M. Mardanova, D. A. Agafonova, R. R. Shusharin, S. N. Shleimovich, M. P. State Inst Appl Opt Sci & Prod Assoc Kazan Russia AN Tupolev Kazan Natl Res Tech Univ KAI Kazan Russia

Subject of study. Results of comparative analysis and testing of the application capabilities of several neural network detection algorithms, programming interfaces, and machine learning libraries for real-time analysis of graphical data from scanning thermal imaging surveying systems are presented. Method. The availability of programming interfaces for integration and adaptation of algorithms into the developed software, data processing rate, and object detection accuracy were selected as the main criteria for assessing the detection algorithms. These criteria were evaluated using practical experiments involving training and running neural network algorithms on test software using computers with different configurations. Main results. Modern neural network algorithms were demon-strated to enable image data processing with detection accuracy for specified object classes, which is sufficient for the automation of image recognition aimed at real-time processing of images from scanning thermal imaging surveying systems. Practical significance. The results of the investigation and tests presented in this study can be advantageous and reduce the time required for developers to find base neural network algorithms suitable for a practical implementation of automation aimed at image processing. Implementation of the considered algorithms in the developed software enables image analysis and processing in real time during surveillance owing to a reduc-tion in the amount of data processed by the operator, thus enabling the removal of the post-processing stage from the technological sequence of the surveillance.(c) 2023 Optica Publishing Group

关键词： image processing image recognition Neural networks Object detection remote sensing Thermal imaging

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GASF-ConvNeXt-TF Algorithm for Perimeter Security Disturbance Identification Based on Distributed Optical Fiber sensing System

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IEEE INTERNET OF THINGS JOURNAL 2024年第10期11卷 17712-17726页

作者： Wang, Ya-Jun Zhuo, Wen Liu, Bin Liu, Juan Hu, Yingying Fu, Yue Xiao, Wenbo He, Xing-Dao Yuan, Jinhui Wu, Qiang Nanchang Hangkong Univ Key Lab Optoelect Informat Sci & Technol Jiangxi P Nanchang 330063 Peoples R China Beijing Univ Posts & Telecommun State Key Lab Informat Photon & Opt Commun Beijing 100876 Peoples R China Northumbria Univ Fac Engn & Environm Newcastle Upon Tyne NE1 8ST England

Phase-sensitive optical time-domain reflectometer technology can transform the fiber-optic cable into a large-scale sensor array for distributed acoustic sensing (DAS), which is an emerging infrastructure for the Internet of Things. However, it is limitated in event recognition capability, which is a major factor preventing its practical field application. This article proposes a perturbation recognition algorithm based on GASF-ConvNeXt-TF with fast process and high recognition accuracy. First, Gramian angular summation field (GASF) algorithm is used to encode external disturbance signal to transform the 1-D time-series signal into a more concentrated 2-D image feature. Then the convolutional neural network model ConvNeXt_tiny network is applied as the classifier. In order to prevent the weight gradient from oscillating back and forth during network training process, a cosine annealing algorithm is introduced to control the decay of the learning rate. Meanwhile, transfer learning is used to further optimize the network model, resulting in higher classification accuracy and faster convergence. Finally, two different experimental scenarios are arranged in a total length of 2.2 km of optical fiber cable, and six different disturbance events (shaking, kicking, knocking, trampling, wheel rolling, and impacting) are set. Different from previous perimeter security disturbance identification experiments, not only single-point disturbance recognition is performed but also two points disturbances are simultaneously recognized, and all have good overall identification accuracy. The overall recognition accuracy of the six disturbance events in single and multiple points experiments are 99.3% and 98.3%, respectively, with an average recognition time of 0.103 s. The proposed technique has potential application in infrastructures structure health monitoring, such as factories, airports, energy pipeline, and highway.

关键词： Sensors Feature extraction Optical fibers Internet of Things Classification algorithms Signal processing algorithms Perturbation methods Convolutional neural network (CNN) distributed acoustic sensing (DAS) pattern recognition transfer learning (TL)

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AutoLC: Search Lightweight and Top-Performing Architecture for remote sensing image Land-Cover Classification 26

AutoLC: Search Lightweight and Top-Performing Architecture f...

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

作者： Zheng, Chenyu Wang, Junjue Ma, Ailong Zhong, Yanfei Wuhan Univ Sch Comp Sci Wuhan Peoples R China Wuhan Univ LIESMARS Wuhan Peoples R China

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

Land-cover classification has long been a hot and difficult challenge in remote sensing community. With massive High-resolution remote sensing (HRS) images available, manually and automatically designed Convolutional Neural Networks (CNNs) have already shown their great latent capacity on HRS land-cover classification in recent years. Especially, the former can achieve better performance while the latter is able to generate lightweight architecture. Unfortunately, they both have shortcomings. On the one hand, because manual CNNs are almost proposed for natural image processing, it becomes very redundant and inefficient to process HRS images. On the other hand, nascent Neural Architecture Search (NAS) techniques for dense prediction tasks are mainly based on encoder-decoder architecture, and just focus on the automatic design of the encoder, which makes it still difficult to recover the refined mapping when confronting complicated HRS scenes. To overcome their defects and tackle the HRS land-cover classification problems better, we propose AutoLC which combines the advantages of two methods. First, we devise a hierarchical search space and gain the lightweight encoder underlying gradient-based search strategy. Second, we meticulously design a lightweight but top-performing decoder that is adaptive to the searched encoder of itself. Finally, experimental results on the LoveDA land-cover dataset demonstrate that our AutoLC method outperforms the state-of-art manual and automatic methods with much less computational consumption.

关键词： Training Gradient methods image processing Computer architecture Manuals Search problems Decoding

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