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10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)

作者： Pal, Mahendra K. Rasmussen, Thorkild M. Abdolmaleki, Mehdi Lulea Univ Technol Dept Civil Environm & Nat Resources Engn Div Geosci & Environm Engn S-97187 Lulea Norrbotten Coun Sweden

ISBN: (纸本)9781728152943

This paper investigates spaceborne multiple multispectral data-fusion and blending to generate an integrated data with higher spatio-spectral resolution and spectral coverage in order to obtain improved geological mapping. A hybrid approach using Gram-Schmidt pan-sharpening and Inverse Distance Weighting (IDW) based downsampling technique is developed to generate integrated data from multiple multispectral data. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat 8, and Sentinel-2 data have been used to evaluate the developed approach for lithological mapping. Liikavaara to Puoltikasvaara including Nautanen and nearby-mining area, in the Gallivare district of Norrbotten county, Sweden, is chosen as a case study. Lithological map of the study area is produced using Support Vector Machine (SVM) classifier. Bedrock geological map from the Geological Survey of Sweden (SGU) is used for classification accuracy assessment. The results show that integrated data produced better accuracy than original individual spaceborne multispectral data for lithological mapping of the study area.

关键词： Multispectral spatial and spectral resolution data fusion and integration classification geological mapping SVM

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Satellite Remote Sensing and Non-Destructive Testing Methods for Transport Infrastructure Monitoring: Advances, Challenges and Perspectives

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REMOTE SENSING 2023年第2期15卷 418-418页

作者： Gagliardi, Valerio Tosti, Fabio Ciampoli, Luca Bianchini Battagliere, Maria Libera D'Amato, Luigi Alani, Amir M. Benedetto, Andrea Roma Tre Univ Dept Engn Via Vito Volterra 62 I-00146 Rome Italy Univ West London UWL Sch Comp & Engn St Marys Rd London W5 5RF England Univ West London UWL Faringdon Res Ctr Nondestruct Testing & Remote Sen St Marys Rd London W5 5RF England Italian Space Agcy ASI Via Politecn I-00133 Rome Italy Kingston Univ London Fac Engn Comp & Environm Penrhyn Rd CampusPenrhyn Rd London KT1 2EE England

High-temporal-frequency monitoring of transport infrastructure is crucial to facilitate maintenance and prevent major service disruption or structural failures. Ground-based non-destructive testing (NDT) methods have been successfully applied for decades, reaching very high standards for data quality and accuracy. However, routine campaigns and long inspection times are required for data collection and their implementation into reliable infrastructure management systems (IMSs). On the other hand, satellite remote sensing techniques, such as the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) method, have proven effective in monitoring ground displacements of transport infrastructure (roads, railways and airfields) with a much higher temporal frequency of investigation and the capability to cover wider areas. Nevertheless, the integration of information from (i) satellite remote sensing and (ii) ground-based NDT methods is a subject that is still to be fully explored in civil engineering. This paper aims to review significant stand-alone and combined applications in these two areas of endeavour for transport infrastructure monitoring. The recent advances, main challenges and future perspectives arising from their mutual integration are also discussed.

关键词： remote sensing non-destructive testing (NDT) transport infrastructure monitoring infrastructure management systems (IMSs) Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) data fusion and integration

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Taxonomizer: Interactive Construction of Fully Labeled Hierarchical Groupings from Attributes of Multivariate data

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2020年第9期26卷 2875-2890页

作者： Mahmood, Salman Mueller, Klaus SUNY Stony Brook Dept Comp Sci Visual Analyt & Imaging Lab Stony Brook NY 11794 USA SUNY Korea Incheon 21985 South Korea

Organizing multivariate data spaces by their dimensions or attributes can be a rather difficult task. Most of the work in this area focuses on the statistical aspects such as correlation clustering, dimension reduction, and the like. These methods typically produce hierarchies in which the leaf nodes are labeled by the attribute names while the inner nodes are often represented by just a statistical measure and criterion, such as a threshold. This makes them difficult to understand for mainstream users. Taxonomies in science, biology, engineering, etc. on the other hand, are easy to comprehend since they provide meaningful labels at the inner nodes as well. Labeling inner nodes of taxonomies automatically requires the identification of hypernyms. Our proposed framework, called Taxonomizer, takes a visual analytics approach to meet this challenge. It appeals to the wisdom of humans to liaise with state of the art data analytics, neural word embeddings, and lexical databases. It consists of a set of visual tools that starts out with an automatically computed hierarchy where the leaf nodes are the original data attributes, and it then allows users to sculpt high-quality taxonomies for any multivariate dataset.

关键词： Taxonomy Semantics Tools Task analysis Aerospace electronics Correlation Labeling High-dimensional data data fusion and integration hierarchy data taxonomy neural embeddings lexical databases

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Dynamic Collaborative Visualization Ecosystem to Support the Analysis of Large-Scale Disparate data

Dynamic Collaborative Visualization Ecosystem to Support the...

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IEEE International Conference on Big data (Big data)

作者： Koehler, Christopher Berger, Andrew Rajashekar, Raksha Wischgoll, Thomas Su, Simon Universal Technol Corp Data Analyt Div Dayton OH 45432 USA Wright State Univ Comp Sci Dept Dayton OH 45435 USA Us Army Res Lab DoD Supercomp Resource Ctr Adelphi MD USA

ISBN: (纸本)9781728108582

There is no one display device or software package that is ideally suited for interactively visualizing related nonspatial, 1D, 2D, 3D, and 4D datasets This is a major drawback, as the benefits of interactive visualization and advanced display technologies cannot be brought to hear. The Dynamic Collaborative Visualization Ecosystem (DynaCoVE) framework addresses this limitation by unifying SciVis, InfoVis, and display technology tools. Pre-existing packages are wrapped as DynaCoVE clients. This entails tracking user interactions with their GUI's and also automatically updating them in response to cues from the DynaCoVE server. The DynaCoVE server then acts as a message broker between many collaborating clients. A messaging protocol was defined based on ZcroMQ's majordomo protocol to enable efficient communication between clients and the server. Thus, visualization software packages that were never intended to be used together can be linked to perform cross display visual analytics. The system is intended to be data and user centric while remaining software, algorithm and display agnostic. This is accomplished in part by providing a common meta visualization graph interface to setup cross-display visualizations. To date DynaCoVE clients for Looker, Visit, and VTK have been tested together with traditional monitors, tiled displays, and an immersive CAVE-type system.

关键词： data fusion and integration large and high-res displays collaborative and distributed visualization integrating spatial and non-spatial data visualization coordinated and multiple views

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integration of Diverse data Sources for Spatial PM2.5 data Interpolation

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IEEE TRANSACTIONS ON MULTIMEDIA 2017年第2期19卷 408-417页

作者： Tang, Mengfan Wu, Xiao Agrawal, Pranav Pongpaichet, Siripen Jain, Ramesh Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Southwest Jiaotong Univ Sch Informat Sci & Technol Chengdu 610031 Peoples R China

Heterogeneous data fusion from disparate geospatial sensors has drawn increasing attention in multimedia. Unfortunately, environmental sensors are usually sparsely and preferentially located, which restricts situation recognition of geographical regions and results in uncertainty in derived inferences. Spatial interpolation is an effective way to solve the problem of data sparsity, which demands the availability of related data sources. However, these data sources are usually in different resolutions, distributions, scales, and densities, which poses a major challenge in data integration. To address this problem, we present a novel spatial interpolation framework to incorporate diverse data sources and model the spatial processes explicitly at multiple resolutions. Spectral analysis is deployed to generate features at multiple spatial resolutions and to improve the interpolation accuracy at unobserved locations. A statistical operator based on the spatial Gaussian process is implemented and integrated into a geospatial situation recognition system, which can analyze heterogeneous spatio- temporal data streams derived from sensors. To verify the effectiveness and efficiency of the proposed framework, this framework is applied to the PM2.5 air pollution application. Experiments conducted in California, USA, demonstrate that the proposed method outperforms state-of-theart approaches.

关键词： Diverse data data fusion and integration eventShop operators geospaital interpolation PM2.5

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Large-Scale Overlays and Trends: Visually Mining, Panning and Zooming the Observable Universe

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2014年第7期20卷 1048-1061页

作者： Luciani, Timothy Basil Cherinka, Brian Oliphant, Daniel Myers, Sean Wood-Vasey, W. Michael Labrinidis, Alexandros Marai, G. Elisabeta Univ Pittsburgh Dept Comp Sci Pittsburgh PA 15206 USA Univ Pittsburgh Dept Phys & Astron Pittsburgh PA 15206 USA Google Pittsburgh PA 15217 USA

We introduce a web-based computing infrastructure to assist the visual integration, mining and interactive navigation of large-scale astronomy observations. Following an analysis of the application domain, we design a client-server architecture to fetch distributed image data and to partition local data into a spatial index structure that allows prefix-matching of spatial objects. In conjunction with hardware-accelerated pixel-based overlays and an online cross-registration pipeline, this approach allows the fetching, displaying, panning and zooming of gigabit panoramas of the sky in real time. To further facilitate the integration and mining of spatial and non-spatial data, we introduce interactive trend images-compact visual representations for identifying outlier objects and for studying trends within large collections of spatial objects of a given class. In a demonstration, images from three sky surveys (SDSS, FIRST and simulated LSST results) are cross-registered and integrated as overlays, allowing cross-spectrum analysis of astronomy observations. Trend images are interactively generated from catalog data and used to visually mine astronomy observations of similar type. The front-end of the infrastructure uses the web technologies WebGL and HTML5 to enable cross-platform, web-based functionality. Our approach attains interactive rendering framerates;its power and flexibility enables it to serve the needs of the astronomy community. Evaluation on three case studies, as well as feedback from domain experts emphasize the benefits of this visual approach to the observational astronomy field;and its potential benefits to large scale geospatial visualization in general.

关键词： data fusion and integration scalability issues geographic/geospatial visualization

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The Five Ws for Information Visualization with Application to Healthcare Informatics

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2013年第11期19卷 1895-1910页

作者： Zhang, Zhiyuan Wang, Bing Ahmed, Faisal Ramakrishnan, I. V. Zhao, Rong Viccellio, Asa Mueller, Klaus SUNY Stony Brook Dept Comp Sci Visual Analyt & Imaging Lab Stony Brook NY 11794 USA SUNY Stony Brook Dept Comp Sci Appl Log Lab Stony Brook NY 11794 USA SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA Stony Brook Univ Hosp Dept Emergency Med Stony Brook NY 11790 USA

The Five Ws is a popular concept for information gathering in journalistic reporting. It captures all aspects of a story or incidence: who, when, what, where, and why. We propose a framework composed of a suite of cooperating visual information displays to represent the Five Ws and demonstrate its use within a healthcare informatics application. Here, the who is the patient, the where is the patient's body, and the when, what, why is a reasoning chain which can be interactively sorted and brushed. The patient is represented as a radial sunburst visualization integrated with a stylized body map. This display captures all health conditions of the past and present to serve as a quick overview to the interrogating physician. The reasoning chain is represented as a multistage flow chart, composed of date, symptom, data, diagnosis, treatment, and outcome. Our system seeks to improve the usability of information captured in the electronic medical record (EMR) and we show via multiple examples that our framework can significantly lower the time and effort needed to access the medical patient information required to arrive at a diagnostic conclusion.

关键词： Visual knowledge representation data fusion and integration coordinated and multiple views focus and context health informatics electronic medical record (EMR) electronic health record (EHR)

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data repository mapping for influenza protein sequence analysis

Data repository mapping for influenza protein sequence analy...

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Conference on Visualization and data Analysis 2011

作者： Pellegrino, Donald Chen, Chaomei Drexel Univ Coll Informat Sci & Technol Philadelphia PA 19104 USA

ISBN: (纸本)9780819484055

This paper introduces a new method for creating an interactive sequence similarity map of all known influenza virus protein sequences and integrating the map with existing general purpose analytical tools. The NCBI data model was designed to provide a high degree of interconnectedness amongst data objects. Substantial and continuous increase in data volume has led to a large and highly connected information space. Researchers seeking to explore this space are challenged to identify a starting point. They often choose data that is popular in the literature. Reference in the literature follow a power law distribution and popular data points may bias explorers toward paths that lead only to a dead-end of what is already known. To help discover the unexpected we developed an interactive visual analytics system to map the information space of influenza protein sequence data. The design is motivated by the needs of eScience researchers.

关键词： Visual Knowledge Discovery Bioinformatics Visualization data fusion and integration data Aggregation

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Potential of IKONOS images for updating land-use base map of China through data fusion and integration

Potential of IKONOS images for updating land-use base map of...

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Conference on Image Processing and Pattern Recognition in Remote Sensing II

作者： Sun, DF Li, H China Agr Univ Coll Nat Resources & Environm Sci Beijing 100094 Peoples R China

ISBN: (纸本)0819456187

The aim of this study was to develop and test a simple and efficient data fusion and integration procedure for updating land-use base map in China with high spatial resolution (1-4m) IKONOS images. The data fusion and integration procedure was divided three stages: (1) using image merger method to generate hybrid 1m spatial resolution multi-spectral IKONOS images to enhance further image analysis. Compared with IHS (Intensity-hue-saturation), PCS (Principal component substitution), Brovey transform and Wavelets transform, SVR (Synthetic variable ratio) merging method was the best method and used based on the merging results. (2) integration of spectral, textural feature and land-use type of former base map basing on rule-classifier to detect possible land-use change. (3) Under the GIS (Geographic information system) environments, the possible change areas were checked by visual interpretation to ascertain the change and land use type through screen overlay of images and land-use base map. The procedures were tested in Liangxiang town, the suburb of Beijing city using IKONOS images of the May 9th, 2001 in 2002. The IKONOS images can satisfied the updating land-use base map of China according the pilot results of the geometric position accuracy, types accuracy and areas accuracy of change regions.

关键词： IKONOS images data fusion and integration land-use base map rule-classifier GIS

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