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ELECTRONICS LETTERS 2022年第13期58卷 508-510页

作者： Zeng, Zhiqiang Tan, Xiaoheng Chen, Zhanye Huang, Yan Tang, Shiyang Wan, Jun Chongqing Univ Sch Microelect & Commun Engn Chongqing Peoples R China Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing Peoples R China Xidian Univ Natl Lab Radar Signal Proc Xian Peoples R China

Synthetic aperture radar (SAR) image similarity metric is at the core of SAR image interpretation techniques, however, it is still a challenging task due to complex nonlinear intensity, scale, and rotation differences between SAR images and other remote sensing images. This letter addresses this problem by proposing a novel similarity metric method for SAR images using structure and shape properties. The magnitude and orientation representation of the phase congruency model is first built based on the local phase of images. Then a new scale and rotation-invariant local binary pattern (SRI-LBP) descriptor is proposed using local structure and shape information. Finally, a similarity metric is defined using the symmetry Kullback Leibler divergence (SKLD) of the SRI-LBP descriptors. Numerical experiment results verify its robustness in terms of nonlinear intensity, scale, and rotation differences.

关键词： radar imaging remote sensing SAR images synthetic aperture radar image similarity metric novel similarity metric method robust image similarity metric method rotation differences feature extraction SAR image interpretation techniques image texture remote sensing images synthetic aperture radar geophysical image processing image classification

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Distribution pattern of potential fishing zones in the Bangka Strait waters: An application of the remote sensing technique

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EGYPTIAN JOURNAL OF remote sensing AND SPACE SCIENCES 2022年第1期25卷 257-265页

作者： Fauziyah Setiawan, Agung Agustriani, Fitri Rozirwan Melki Ningsih, Ellis Nurjuliasti Ulqodry, T. Zia Sriwijaya Univ Fac Math & Nat Sci Marine Sci Dept Jl Raya Palembang Prabumulih KM 32 Ogan Ilir Regency 30862 Province South Indonesia

Information regarding fishing grounds was needed to assist fishermen in their fishing activities. Information about the sea surface chlorophyll-a concentration (SSCC) and sea surface temperature (SST) could be used as a reference to identify potential fishing zones (PFZ). The purpose of this study was to identify SST and SSCC data using MODIS (Moderate Resolution Imaging Spectroradiometer) satel-lite imagery for determining the PFZ and analyzing their distribution pattern seasonally. The determina-tion of the PFZ point was carried out by overlaying the SSCC and SST data based on the results of image data processing. The results showed that the distribution pattern of PFZ points in the Bangka Strait waters was predominantly found in the Banyuasin waters. The distribution pattern of PFZ points in the dry sea -son (June-August) and transition season II (September-November) had the same pattern and tended to dominate the coastal areas of the waters. The distribution patterns in the wet season (December-February) and transition season I (March-May) spread throughout the Bangka Strait waters. The most PFZ points were found in transition season I (636 PFZ points), while the minor PFZ points were found in transition season II (219 PFZ points). Integrating the remote sensing and GIS technique with statistical validation tests were useful and became a simple method for identifying the PFZ distribution. However, validating the PFZ distributions using the catch data was required.(c) 2021 National Authority of remote sensing & Space Science. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： Chlorophyll-a GIS MODIS Sea surface temperature Seasonal variability

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Crowd Movement Type Estimation in Video by Integral Optical Flow and Convolution Neural Network

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pattern recognition AND image ANALYSIS 2024年第2期34卷 266-274页

作者： Chen, Huafeng Pashkevich, Angelina Ye, Shiping Bohush, Rykhard Ablameyko, Sergey Zhejiang Shuren Univ Hangzhou 310015 Peoples R China Int Sci & Technol Cooperat Base Zhejiang Prov Remote Sensing Image Proc & Applicat Hangzhou Peoples R China Belarusian State Univ Minsk 220030 BELARUS Polotsk State Univ Novopolotsk 211440 BELARUS Natl Acad Sci Belarus United Inst Informat Problems Minsk 220012 BELARUS

The paper proposes a new approach for crowd movement type estimation in video by combining convolutional neural network and integral optical flow. At first, main notions of crowd detection and tracking are given. Secondly, crowd movement features and parameters are defined. Three rules are proposed to identify direct crowd motion. Signs are presented for identifying chaotic crowd movement. Region movement indicators are introduced to analyze the movement of a group of people or a crowd. Thirdly, an algorithm of crowd movement types estimation using convolutional neural network and integral optical flow is proposed. We calculate crowd movement trajectories and show how they can be used to analyze behavior and divide crowds into groups of people. Experimental results show that with the help of convolutional neural network and integral optical flow crowd movement parameters can be calculated more accurately and quickly. The algorithm demonstrates stronger robustness to noise and the ability to get more accurate boundaries of moving objects.

关键词： optical flow convolutional neural network crowd motion analysis video surveillance

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Nonuniformly Dehaze Network for Visible remote sensing images

Nonuniformly Dehaze Network for Visible Remote Sensing Image...

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

作者： Chen, Zhaojie Li, Qi Feng, Huajun Xu, Zhihai Chen, Yueting Zhejiang Univ Coll Opt Sci & Engn Hangzhou Peoples R China

ISBN: (数字)9781665487399

ISBN: (纸本)9781665487399

Nonuniform haze on remote sensing images degrades image quality and hinders many high-level tasks. In this paper, we propose a Nonuniformly Dehaze Network towards nonuniform haze on visible remote sensing images. To extract robust haze-aware features, we propose Nonuniformly Excite (NE) module. Inspired by the well-known gather-excite attention module, NE module works in a map-excite manner. In the map operation, we utilize a proposed Dual Attention Dehaze block to extract local enhanced features. In the gather operation, we utilize a strided deformable convolution to nonuniformly process features and extract nonlocal haze-aware features. In the excite operation, we employ a pixel-wise attention between local enhanced features and nonlocal haze-aware features, to gain finer haze-aware features. Moreover, we recursively embed NE modules in a multi-scale framework. It helps not only significantly reduce network's parameters, but also recursively deliver and fuse haze-aware features from higher levels, which makes learning more efficient. Experiments demonstrate that the proposed network performs favorably against the state-of-the-art methods on both synthetic and real-world images.

关键词： image quality Computer vision Fuses Convolution Conferences Feature extraction pattern recognition

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Multi-scale Siamese Dual Decoding Network for remote sensing Tank image Segmentation 23

Multi-scale Siamese Dual Decoding Network for Remote Sensing...

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6th International Conference on Signal processing and Machine Learning, SPML 2023

作者： Liu, Jiashan Zhang, Shanwen Wang, Xuqi Ren, Zhiwen Ding, Zili The School of Electronic Information Xijing University China

ISBN: (纸本)9798400707575

To address the multi-scale problem and interference of differences between data in remote sensing image segmentation, a multi-scale Siamese dual decoding network is proposed. The twin network is used as the backbone network for parallel processing and weight sharing. The dilated stratification attention module is designed to extract effective multi-scale features. Different branches can obtain receptive fields of different sizes in order to learn more partition features and suppress background interference. The global attention module is designed to extract common characteristics between datasets and to focus on the characteristics of the datasets, thus extracting connections between datasets. The residual fusion module is also designed for adaptive learning of feature weights of different encoding layers. Experimental results demonstrate that the proposed method has better efficacy and robustness compared to classical networks, and has stronger recognition efficacy for different complexity scenarios, meeting the expectations of this study. © 2023 ACM.

关键词： image segmentation

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Scientific Gateway for Evaluating Land-Surface Temperatures Using Landsat 8 and Meteorological Data over Armenia and Belarus

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pattern recognition AND image ANALYSIS 2024年第2期34卷 225-236页

作者： Abrahamyan, R. Belotserkovsky, A. Lukashevich, P. Gevorgyan, A. Grigoryan, H. Astsatryan, H. Natl Acad Sci Republ Armenia Inst Informat & Automat Problems Yerevan 0014 Armenia Natl Acad Sci United Inst Informat Problems Minsk 220012 BELARUS Hydrometeorol & Monitoring Ctr Climate Serv Div Erevan 0025 Armenia

The article introduces a scientific gateway to assess land surface temperatures using Landsat 8 and visible infrared imaging radiometer suite data. The gateway offers a selection of four temperature retrieval algorithms and two interpolation methods to create time series. The evaluation of the gateway's performance in Armenia from May to October 2022 is illustrated. The research identifies the Price, Jimenez-Munoz, McMillin, and I05 Chanel algorithms as the most accurate nighttime temperature estimation. Additionally, these products exhibit a reasonable level of accuracy, with an average root mean squared error ranging from 2.42 to 2.45 degrees C and a coefficient of determination spanning from 0.82 to 0.95. The outcomes of this study bear significant relevance for diverse applications such as urban heat island analysis, environmental monitoring, and agricultural assessments.

关键词： land surface temperature visible infrared imaging radiometer suite landsat remote sensing scientific gateway

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Automated image processing for remote sensing Data Classification 26th

Automated Image Processing for Remote Sensing Data Classifi...

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26th International Conference on pattern recognition, ICPR 2022

作者： Reggiannini, Marco Papini, Oscar Pieri, Gabriele Institute of Information Science and Technologies National Research Council of Italy Via G. Moruzzi 1 Pisa56124 Italy

ISBN: (纸本)9783031377419

remote sensing technologies allow for continuous and valuable monitoring of the Earth’s various environments. In particular, coastal and ocean monitoring presents an intrinsic complexity that makes such monitoring the main source of information available. Oceans, being the largest but least observed habitat, have many different factors affecting theirs faunal variations. Enhancing the capabilities to monitor and understand the changes occurring allows us to perform predictions and adopt proper decisions. This paper proposes an automated classification tool to recognise specific marine mesoscale events. Typically, human experts monitor and analyse these events visually through remote sensing imagery, specifically addressing Sea Surface Temperature data. The extended availability of this kind of remote sensing data transforms this activity into a time-consuming and subjective interpretation of the information. For this reason, there is an increased need for automated or at least semi-automated tools to perform this task. The results presented in this work have been obtained by applying the proposed approach to images captured over the southwestern region of the Iberian Peninsula. © 2023, Springer Nature Switzerland AG.

关键词： Surface temperature

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RF Drone Detection System Based on a Distributed Sensor Grid With remote Hardware-Accelerated Signal processing

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IEEE ACCESS 2023年 11卷 138759-138772页

作者： Flak, Przemyslaw Czyba, Roman Silesian Tech Univ Fac Automat Control Elect & Comp Sci Dept Automat Control & Robot PL-44100 Gliwice Poland Silesian Tech Univ Fac Automat Control Elect & Comp Sci Dept Automat Control & Robot PL-44100 Gliwice Poland

Unmanned Aerial Vehicles (UAVs), sometimes known as drones, evolved from military to civilian applications, opening up novel perspectives in a variety of everyday services. The rapidly growing consumer interest in amateur drones equipped with high-end cameras compromises the everyday safety and privacy of people. In the literature, a variety of sensing techniques based on different physical phenomena have been proposed for drone detection. Among acoustic, optical, or radar detection systems, passive radiofrequency sensing is the only one that can identify a drone even before it takes off and additionally indicate the operator's location. A spectrogram-based method is developed and optimised in terms of computing location, resulting in the possibility of sensor grid deployment over a standard Ethernet network. The detection phase involves hardware-accelerated energy sensing to extract the data frames from the background noise. Drone presence is then identified using machine learning based solely on preamble pattern recognition, which reduces the computational effort. The presented procedure is evaluated in an isolated setting employing an open-source dataset and tuned across multiple neural network architectures. Next, the complete sensor processing chain is examined in a real-life scenario. The analytical energy detector stage reaches a margin of roughly -8.7 dB in the signal-to-noise (SNR) ratio. With 1.1 M parameters, the proposed neural network achieves 99.93% simulation accuracy in up to -9.5 dB SNR range. Even after quantization for embedded platform implementation, the device can be used as a stand-alone early intrusion detector or as part of a distributed sensor grid.

关键词： Convolutional neural network drones field programmable gate array software defined radio spectrogram surveillance unmanned aerial vehicles

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Information Fusion for Classification of Hyperspectral and LiDAR Data Using IP-CNN

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IEEE TRANSACTIONS ON GEOSCIENCE AND remote sensing 2022年 60卷 1页

作者： Zhang, Mengmeng Li, Wei Tao, Ran Li, Hengchao Du, Qian Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Southwest Jiaotong Univ Sichuan Prov Key Lab Informat Coding & Transmiss Chengdu 610031 Peoples R China Mississippi State Univ Dept Elect & Comp Engn Starkville MS 39762 USA

Joint use of multisensor information has attracted considerable attention in the remote sensing community. While applications in land-cover observation benefit from information diversity, multisensor integration technique is confronted with many challenges, including inconsistent size of data, different data structures, uncorrelated physical properties, and scarcity of training data. In this article, an information fusion network, named interleaving perception convolutional neural network (IP-CNN), is proposed for integrating heterogeneous information and improving joint classification performance of hyperspectral image (HSI) and light detection and ranging (LiDAR) data. Specifically, a bidirectional autoencoder is designed to reconstruct hyperspectral and LiDAR data together, and the reconstruction process is trained with no dependence upon annotated information. Both HSI-perception constraint and LiDAR-perception constraint are imposed on multisource structural information integration. Accordingly, fused data are fed into a two-branch CNN for final classification. To validate the effectiveness of the model, the experiments were conducted using three datasets (i.e., Muufl Gulfport data, Trento data, and Houston data). The final results demonstrate that the proposed framework can significantly outperform state-of-the-art methods even with small-size training samples.

关键词： Laser radar Feature extraction Hyperspectral imaging Task analysis image reconstruction Training data Distance measurement Convolutional neural network (CNN) deep learning hyperspectral image (HSI) joint classification light detection and ranging (LiDAR) data pattern recognition remote sensing

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Railway facilities safety monitoring system based on space-air-ground multi-source data

Railway facilities safety monitoring system based on space-a...

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

作者： Xue, Aliang Guo, Hang Zhang, Na Miao, Long China Coal Aerial Remote Sensing Group Co. Ltd. 216 Shenzhou Fourth Road Xi'an China Xi'an Rail Transit Group Co. Ltd. 126 Fengcheng 8th Road Xi'an China

ISBN: (纸本)9781510666351

In view of many problems existing in the current railway transportation process, such as pedestrian crossing the guardrail, landslide, rock rolling, debris flow and other factors, this paper summarizes the research status of the underground railway foreign body intrusion monitoring system at home and abroad, and puts forward a railway facility safety monitoring system based on multi-source data from the sky and the sky. The system uses satellite, unmanned aerial vehicle, distributed optical fiber sensor, video, GNSS and GIS technologies to realize the safety monitoring of railway facilities, and formulates the technical scheme for the safety information management of space-based railway facilities, and tests and evaluates the research results through the test section. The development trend of railway facilities safety monitoring system is prospected as follows: foreign body intrusion monitoring at the boundary of key sections;Real-time monitoring of tunnel entrance and high slope section stones;Carry out dynamic monitoring of off-road safety environment. © 2023 SPIE.

关键词： Optical fibers

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