检索结果-内蒙古大学图书馆

Toward On-Board Panoptic Segmentation of multispectral Satellite images

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Fernando, Tharindu Fookes, Clinton Gammulle, Harshala Denman, Simon Sridharan, Sridha Queensland Univ Technol Signal Proc Artificial Intelligence & VisionTechno Brisbane Qld 4000 Australia

With tremendous advancements in low-power embedded computing devices and remote sensing instruments, the traditional satellite image processing pipeline which includes an expensive data transfer step prior to processing data on the ground is being replaced by on- board processing of captured data. This paradigm shift enables critical and time-sensitive intelligence to be acquired in a timely manner on- board the satellite itself. However, at present, the on- board processing of multispectral satellite images is limited to classification and segmentation tasks. Extending this processing to the next logical level, we take the first step toward on- board panoptic segmentation of multispectral satellite images and evaluate the applicability of state-of-the-art panoptic segmentation models to an on- board setting. Panoptic segmentation offers major economic and environmental insights, ranging from yield estimation from agricultural lands to intelligence for complex military applications. Nevertheless, the on- board intelligence extraction poses several challenges due to the loss of temporal observations and the need to generate predictions from a single sample. To address this challenge, we propose a multimodal teacher network with a cross modality attention-based fusion strategy to improve segmentation accuracy by exploiting data from multiple modes. We also propose an online knowledge distillation framework to transfer the knowledge learned by this multimodal teacher network to a unimodal student, which receives only a single frame input, and is more appropriate for an on- board environment. We benchmark our approach against existing state-of-the-art panoptic segmentation models using the PASTIS multispectral panoptic segmentation dataset considering an on- board processing setting. Our evaluations demonstrate a substantial 10.7%, 11.9%, and 10.6% increase in segmentation quality (SQ), recognition quality (RQ), and panoptic quality (PQ) metrics compared to the existing

关键词： image segmentation Satellites Computer architecture Benchmark testing Pipelines Knowledge engineering Task analysis Knowledge distillation multimodality fusion multispectral image processing on-board satellite image processing panoptic segmentation

来源：评论

学校读者我要写书评

暂无评论

Concept of a Robotic System for Autonomous Coarse Waste Recycling 18

Concept of a Robotic System for Autonomous Coarse Waste Recy...

引用

18th International Conference on Informatics in Control, Automation and Robotics (ICINCO)

作者： Tiedemann, Tim Keppner, Matthis Runge, Tom Voegele, Thomas Wittmaier, Martin Wolff, Sebastian Univ Appl Sci Hamburg Dept CS Berliner Tor 7 Hamburg Germany Robot Innovat Ctr German Res Ctr Artificial Intelligence Bremen Germany Bremen Univ Appl Sci Inst Energy Recycling & Environm Protect Bremen Germany

ISBN: (纸本)9789897585227

The recycling of coarse waste such as construction and demolition waste (CDW), bulky waste, etc., is a process that is currently performed mechanically and manually. Unlike packaging waste, commercial waste and the like, which is usually cut or shredded into small pieces and then automatically separated and sorted on conveyor belt-based systems, coarse waste is separated by specialized personnel using wheel loaders, cranes or excavators. This paper presents the concept of a robotic system designed to autonomously separate recyclable coarse materials from bulky waste, demolition and construction waste, etc. The proposed solution explicitly uses existing heavy equipment (e.g., an excavator currently in use on-site) rather than developing a robot from scratch. A particular focus is set on the sensory system options used to identify and classify waste objects.

关键词： Smart Recycling Construction Waste Recycling multispectral image processing Mobile Robotics

来源：评论

学校读者我要写书评

暂无评论

A Novel multispectral Line Segment Matching Method Based on Phase Congruency and Multiple Local Homographies

引用

REMOTE SENSING 2022年第16期14卷 3857-3857页

作者： Hu, Haochen Li, Boyang Yang, Wenyu Wen, Chih-Yung Hong Kong Polytech Univ Dept Aeronaut & Aviat Engn Hung Hom Kowloon Hong Kong Peoples R China

Feature matching is a fundamental procedure in several image processing methods applied in remote sensing. multispectral sensors with different wavelengths can provide complementary information. In this work, we propose a multispectral line segment matching algorithm based on phase congruency and multiple local homographies (PC-MLH) for image pairs captured by the cross-spectrum sensors (visible spectrum and infrared spectrum) in man-made scenarios. The feature points are first extracted and matched according to phase congruency. Next, multi-layer local homographies are derived from clustered feature points via random sample consensus (RANSAC) to guide line segment matching. Moreover, three geometric constraints (line position encoding, overlap ratio, and point-to-line distance) are introduced in cascade to reduce the computational complexity. The two main contributions of our work are as follows: First, compared with the conventional line matching methods designed for single-spectrum images, PC-MLH is robust against nonlinear radiation distortion (NRD) and can handle the unknown multiple local mapping, two common challenges associated with multispectral feature matching. Second, fusion of line extraction results and line position encoding for neighbouring matching increase the number of matched line segments and speed up the matching process, respectively. The method is validated using two public datasets, CVC-multimodal and VIS-IR. The results show that the percentage of correct matches (PCM) using PC-MLH can reach 94%, which significantly outperforms other single-spectral and multispectral line segment matching methods.

关键词： multispectral image processing line segment matching phase congruency

来源：评论

学校读者我要写书评

暂无评论

Oil Palm Detection via Deep Transfer Learning

Oil Palm Detection via Deep Transfer Learning

引用

IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Bonet, Isis Caraffini, Fabio Pena, Alejandro Puerta, Alejandro Gongora, Mario EIA Univ Envigado Colombia De Montfort Univ Leicester Leics England

ISBN: (数字)9781728169293

ISBN: (纸本)9781728169293

This article presents an intelligent system using deep learning algorithms and the transfer learning approach to detect oil palm units in multispectral photographs taken with unmanned aerial vehicles. Two main contributions come from this piece of research. First, a dataset for oil palm units detection is carefully produced and made available online. Although being tailored to the palm detection problem, the latter has general validity and can be used for any classification application. Second, we designed and evaluated a state-of-the-art detection system, which uses a convolutional neural network to extract meaningful features, and a classifier trained with the images from the proposed dataset. Results show outstanding effectiveness with an accuracy peak of 99.5% and a precision of 99.8%. Using different images for validation taken from different altitudes the model reached an accuracy of 97.5% and a precision of 98.3%. Hence, the proposed approach is highly applicable in the field of precision agriculture.

关键词： Classification Convolutional neural networks Deep learning Oil palm multispectral image processing

来源：评论

学校读者我要写书评

暂无评论

Design and evaluation of a parallel and optimized light-tissue interaction-based method for fast skin lesion assessment

引用

JOURNAL OF REAL-TIME image processing 2018年第2期15卷 407-420页

作者： Li, Chao Brost, Vincent Benezeth, Yannick Marzani, Franck Yang, Fan Univ Burgundy CNRS Lab LE2I 6306 F-21078 Dijon France

In recent years, image processing technics have attracted much attention as powerful tools in the assessment of skin lesions from multispectral images. The Kubelka-Munk Genetic Algorithm (KMGA) is a novel method which has been developed for this diagnostic purpose. It combines the Kubelka-Munk light-tissue interaction model with the Genetic Algorithm optimization process, and allows quantitative measure of cutaneous tissue by computing skin parameter maps such as melanin concentration, volume blood fraction, oxygen saturation or epidermis/dermis thickness. However, its efficiency is seriously reduced by the mass floating-point operations for each pixel of the multispectral image, and this prevents the algorithm from reaching industrial standards related to cost, power and speed for clinical applications. In this paper, our work focuses on the improvement of this theoretical achievement. Therefore, we repropose a new C-based Parallel and Optimized KMGA (PO-KMGA) technique designed and optimized using multiple ways: KM model optimized re-writing, operation massively parallelized using POSIX threads, memory use optimization and routine pipelining with Intel C++ Compiler, etc. Intensive experiments demonstrate that our introduced PO-KMGA framework spends less than 10 min to finish a job that the conventional KMGA spends around two days to do in the same hardware environment with a similar algorithm performance.

关键词： multispectral image processing Kubelka-Munk model Genetic algorithm Function parallelization High-performance computer POSIX threads

来源：评论

学校读者我要写书评

暂无评论

Registration of multispectral 3D points for plant inspection

引用

PRECISION AGRICULTURE 2018年第3期19卷 513-536页

作者： Liu, Huajian Lee, Sang-Heon Chahl, Javaan Singh Univ South Australia Sch Engn Adelaide SA Australia Def Sci & Technol Org Joint & Operat Anal Div Canberra ACT Australia

Machine vision technologies have shown advantages for efficient and accurate plant inspection in precision agriculture. Regarding the balance between accuracy of inspection and compactness for infield applications, multispectral imaging systems would be more suitable than RGB colour cameras or hyperspectral imaging systems. multispectral image registration (MIR) is a key issue for multispectral imaging systems, however, this task is challenging. First of all, in many cases, two images needing registration do not have a one-to-one linear mapping in 2D space and therefore they cannot be aligned in 2D images. Furthermore, the general MIR algorithms are limited to images with uniform intensity and are incapable of registering images with rich features. This study developed a machine vision system (MVS) and a MIR method which replaces 2D-2D image registration by 3D-3D point cloud registration. The system can register 3D point clouds of ultraviolet (UV), blue, green, red and near-infrared (NIR) spectra in 3D space. It was found that the point clouds of general plants created by images of different spectral bands have a complementary property, and therefore a combined point cloud, called multispectral 3D point cloud, is denser than any cloud created by a single spectral band. Intensity information of each spectral band is available in a multispectral 3D point cloud and therefore image fusion and 3D morphological analysis can be conducted in the cloud. The MVS could be used as a sensor of a robotic system to fulfil on-the-go infield plant inspection tasks.

关键词： multispectral image registration Multimodal image registration 3D reconstruction 3D point registration Plant identification multispectral image processing Computer vision Machine vision image fusion

来源：评论

学校读者我要写书评

暂无评论

Artificial Neural Network to Predict Vine Water Status Spatial Variability Using multispectral Information Obtained from an Unmanned Aerial Vehicle (UAV)

引用

SENSORS 2017年第11期17卷 2488-2488页

作者： Poblete, Tomas Ortega-Farias, Samuel Angel Moreno, Miguel Bardeen, Matthew Univ Talca CITRA Casilla 747 Talca 3460000 Chile Univ Talca Res Program Adaptat Agr Climate Change A2C2 Casilla 747 Talca 3460000 Chile Univ Castilla La Mancha Reg Ctr Water Res Campus Univ S-N Albacete 02071 Spain Univ Talca Fac Ingn Curico 3340000 Chile

Water stress, which affects yield and wine quality, is often evaluated using the midday stem water potential ((stem)). However, this measurement is acquired on a per plant basis and does not account for the assessment of vine water status spatial variability. The use of multispectral cameras mounted on unmanned aerial vehicle (UAV) is capable to capture the variability of vine water stress in a whole field scenario. It has been reported that conventional multispectral indices (CMI) that use information between 500-800 nm, do not accurately predict plant water status since they are not sensitive to water content. The objective of this study was to develop artificial neural network (ANN) models derived from multispectral images to predict the (stem) spatial variability of a drip-irrigated Carmenere vineyard in Talca, Maule Region, Chile. The coefficient of determination (R-2) obtained between ANN outputs and ground-truth measurements of (stem) were between 0.56-0.87, with the best performance observed for the model that included the bands 550, 570, 670, 700 and 800 nm. Validation analysis indicated that the ANN model could estimate (stem) with a mean absolute error (MAE) of 0.1 MPa, root mean square error (RMSE) of 0.12 MPa, and relative error (RE) of -9.1%. For the validation of the CMI, the MAE, RMSE and RE values were between 0.26-0.27 MPa, 0.32-0.34 MPa and -24.2-25.6%, respectively.

关键词： multispectral image processing artificial neural network UAV midday stem water potential

来源：评论

学校读者我要写书评

暂无评论

FILTERS BASED ON AGGREGATION OPERATORS. Part 4. Generalized vector median filters 24

FILTERS BASED ON AGGREGATION OPERATORS. Part 4. Generalized ...

引用

24th International Crimean Conference Microwave & Telecommunication Technology (CriMiCo)

作者： Labunets, V. G. Gainanov, D. N. Arslanova, R. A. Ostheimer, E. Ural Fed Univ Ekaterinburg 620002 Sverdlovsk Regi Russia Capricat LLC Pompano Beach FL 33062 USA

ISBN: (纸本)9789663354125

In this work, a new class of nonlinear filters called vector median aggregation filters for multispectral image processing is introduced and applied to the color image filtering problem. These filters are based on an arbitrary pair of aggregation operators and a selection rule. We show that a large body of vector median filters proposed to date constitute a proper subset of new vector median aggregation filters set.

关键词： image colour analysis median filters aggregation operators color image filtering problem generalized vector median filters multispectral image processing nonlinear filters vector median aggregation filters Measurement Radio frequency

来源：评论

学校读者我要写书评

暂无评论

multispectral MRI segmentation of age related white matter changes using a cascade of support vector machines

引用

JOURNAL OF THE NEUROLOGICAL SCIENCES 2012年第1-2期322卷 211-216页

作者： Damangir, Soheil Manzouri, Amirhossein Oppedal, Ketil Carlsson, Stefan Firbank, Michael J. Sonnesyn, Hogne Tysnes, Ole-Bjorn O'Brien, John T. Beyer, Mona K. Westman, Eric Aarsland, Dag Wahlund, Lars-Olof Spulber, Gabriela Karolinska Inst Div Clin Geriatr Dept Neurobiol Care Sci & Soc S-14186 Stockholm Sweden Karolinska Inst Alzheimers Dis Res Ctr S-14186 Stockholm Sweden Stavanger Univ Hosp Reg Ctr Age Related Med & Coact Stavanger Norway Univ Stavanger Dept Elect Engn & Comp Sci Stavanger Norway Royal Inst Technol Sch Comp Sci & Commun Stockholm Sweden Wolfson Res Ctr Inst Ageing & Hlth Newcastle Upon Tyne Tyne & Wear England Haukeland Hosp Dept Neurol N-5021 Bergen Norway Stavanger Univ Hosp Norwegian Ctr Movement Disorders Stavanger Norway

White matter changes (WMC) are the focus of intensive research and have been linked to cognitive impairment and depression in the elderly. Cumbersome manual outlining procedures make research on WMC labor intensive and prone to subjective bias. We present a fast, fully automated method for WMC segmentation using a cascade of reduced support vector machines (SVMs) with active learning. Data of 102 subjects was used in this study. Two MRI sequences (T1-weighted and FLAIR) and masks of manually outlined WMC from each subject were used for the image analysis. The segmentation framework comprises pre-processing, classification (training and core segmentation) and post-processing. After pre-processing, the model was trained on two subjects and tested on the remaining 100 subjects. The effectiveness and robustness of the classification was assessed using the receiver operating curve technique. The cascade of SVMs segmentation framework outputted accurate results with high sensitivity (90%) and specificity (99.5%) values, with the manually outlined WMC as reference. An algorithm for the segmentation of WMC is proposed. This is a completely competitive and fast automatic segmentation framework, capable of using different input sequences, without changes or restrictions of the image analysis algorithm. (C) 2012 Elsevier B.V. All rights reserved.

关键词： MRI White matter changes SVM Tissue segmentation multispectral image processing

来源：评论

学校读者我要写书评

暂无评论

Smart multispectral imager

Smart Multispectral Imager

引用

25th IEEE Photonics Conference (IPC)

作者： Preissler, Marc Rosenberger, Maik Linss, Gerhard Ilmenau Univ Technol Dept Qual Assurance & Ind Image Proc Ilmenau Germany

The multispectral imager combines parallel FPGA Hardware with modular and high dynamic mechanical design. Integrated processing and an interconnection to all electromechanical parts enable a real time image correction... 详细信息

ISBN: (纸本)9781457707339

关键词： smart imager multispectral image processing FPGA

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：