232 research outputs found
Exploiting Deep Features for Remote Sensing Image Retrieval: A Systematic Investigation
Remote sensing (RS) image retrieval is of great significant for geological
information mining. Over the past two decades, a large amount of research on
this task has been carried out, which mainly focuses on the following three
core issues: feature extraction, similarity metric and relevance feedback. Due
to the complexity and multiformity of ground objects in high-resolution remote
sensing (HRRS) images, there is still room for improvement in the current
retrieval approaches. In this paper, we analyze the three core issues of RS
image retrieval and provide a comprehensive review on existing methods.
Furthermore, for the goal to advance the state-of-the-art in HRRS image
retrieval, we focus on the feature extraction issue and delve how to use
powerful deep representations to address this task. We conduct systematic
investigation on evaluating correlative factors that may affect the performance
of deep features. By optimizing each factor, we acquire remarkable retrieval
results on publicly available HRRS datasets. Finally, we explain the
experimental phenomenon in detail and draw conclusions according to our
analysis. Our work can serve as a guiding role for the research of
content-based RS image retrieval
Heterogeneous sensor database framework for the sensor observation service: integrating remote and in-situ sensor observations at the database backend
Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.Environmental monitoring and management systems in most cases deal with models and
spatial analytics that involve the integration of in-situ and remote sensor observations. In-situ
sensor observations and those gathered by remote sensors are usually provided by different databases and services in real-time dynamic service systems like the Geo-Web Services. Thus,
data have to be pulled from different databases and transferred over the web before they are
fused and processed on the service middleware. This process is very massive and unnecessary
communication and work load on the service, especially when retrieving massive raster
coverage data. Thus in this research, we propose a database model for heterogeneous sensortypes
that enables geo-scientific processing and spatial analytics involving remote and in-situ
sensor observations at the database level of the Sensor Observation Service, SOS. This
approach would be used to reduce communication and massive workload on the Geospatial
Web Service, as well make query request from the user end a lot more flexible. Hence the
challenging task is to develop a heterogeneous sensor database model that enables geoprocessing
and spatial analytics at the database level and how this could be integrated with the
geo-web services to reduce communication and workload on the service and as well make
query request from the client end more flexible through the use of SQL statements
Durability of Wireless Charging Systems Embedded Into Concrete Pavements for Electric Vehicles
Point clouds are widely used in various applications such as 3D modeling, geospatial analysis, robotics, and more. One of the key advantages of 3D point cloud data is that, unlike other data formats like texture, it is independent of viewing angle, surface type, and parameterization. Since each point in the point cloud is independent of the other, it makes it the most suitable source of data for tasks like object recognition, scene segmentation, and reconstruction. Point clouds are complex and verbose due to the numerous attributes they contain, many of which may not be always necessary for rendering, making retrieving and parsing a heavy task.
As Sensors are becoming more precise and popular, effectively streaming, processing, and rendering the data is also becoming more challenging. In a hierarchical continuous LOD system, the previously fetched and rendered data for a region may become unavailable when revisiting it. To address this, we use a non-persistence cache using hash-map which stores the parsed point attributes, which still has some limitations, such as the dataset needing to be refetched and reprocessed if the tab or browser is closed and reopened which can be addressed by persistence caching. On the web, popularly persistence caching involves storing data in server memory, or an intermediate caching server like Redis. This is not suitable for point cloud data where we have to store parsed and processed large point data making point cloud visualization rely only on non-persistence caching.
The thesis aims to contribute toward better performance and suitability of point cloud rendering on the web reducing the number of read requests to the remote file to access data.We achieve this with the application of client-side-based LRU Cache and Private File Open Space as a combination of both persistence and non-persistence caching of data. We use a cloud-optimized data format, which is better suited for web and streaming hierarchical data structures. Our focus is to improve rendering performance using WebGPU by reducing access time and minimizing the amount of data loaded in GPU.
Preliminary results indicate that our approach significantly improves rendering performance and reduce network request when compared to traditional caching methods using WebGPU
Overview of contextual tracking approaches in information fusion
Proceedings of: Geospatial InfoFusion III. 2-3 May 2013 Baltimore, Maryland, United States.Many information fusion solutions work well in the intended scenarios; but the applications, supporting data, and capabilities change over varying contexts. One example is weather data for electro-optical target trackers of which standards have evolved over decades. The operating conditions of: technology changes, sensor/target variations, and the contextual environment can inhibit performance if not included in the initial systems design. In this paper, we seek to define and categorize different types of contextual information. We describe five contextual information categories that support target tracking: (1) domain knowledge from a user to aid the information fusion process through selection, cueing, and analysis, (2) environment-to-hardware processing for sensor management, (3) known distribution of entities for situation/threat assessment, (4) historical traffic behavior for situation awareness patterns of life (POL), and (5) road information for target tracking and identification. Appropriate characterization and representation of contextual information is needed for future high-level information fusion systems design to take advantage of the large data content available for a priori knowledge target tracking algorithm construction, implementation, and application.Publicad
Supporting multi-resolution out-of-core rendering of massive LiDAR point clouds through non-redundant data structures
This is an Accepted Manuscript of an article published by Taylor & Francis in INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE on 28 Nov 2018, available at: https://doi.org/10.1080/13658816.2018.1549734[Abstract]: In recent years, the evolution and improvement of LiDAR (Light Detection and Ranging) hardware has increased the quality and quantity of the gathered data, making the storage, processing and management thereof particularly challenging. In this work we present a novel, multi-resolution, out-of-core technique, used for web-based visualization and implemented through a non-redundant, data point organization method, which we call Hierarchically Layered Tiles (HLT), and a tree-like structure called Tile Grid Partitioning Tree (TGPT). The design of these elements is mainly focused on attaining very low levels of memory consumption, disk storage usage and network traffic on both, client and server-side, while delivering high-performance interactive visualization of massive LiDAR point clouds (up to 28 billion points) on multiplatform environments (mobile devices or desktop computers). HLT and TGPT were incorporated and tested in ViLMA (Visualization for LiDAR data using a Multi-resolution Approach), our own web-based visualization software specially designed to work with massive LiDAR point clouds.This research was supported by Xunta de Galicia under the Consolidation Programme of Competitive Reference Groups, co-founded by ERDF funds from the EU [Ref. ED431C 2017/04]; Consolidation Programme of Competitive Research Units, co-founded by ERDF funds from the EU [Ref. R2016/037]; Xunta de Galicia (Centro Singular de Investigación de Galicia accreditation 2016/2019) and the European Union (European Regional Development Fund, ERDF) under Grant [Ref. ED431G/01]; and the Ministry of Economy and Competitiveness of Spain and ERDF funds from the EU [TIN2016-75845-P].Xunta de Galicia; ED431C 2017/04Xunta de Galicia; R2016/037Xunta de Galicia; ED431G/0
Management of spatial data for visualization on mobile devices
Vector-based mapping is emerging as a preferred format in Location-based
Services(LBS), because it can deliver an up-to-date and interactive map visualization.
The Progressive Transmission(PT) technique has been developed to
enable the ecient transmission of vector data over the internet by delivering
various incremental levels of detail(LoD). However, it is still challenging to apply
this technique in a mobile context due to many inherent limitations of mobile
devices, such as small screen size, slow processors and limited memory. Taking
account of these limitations, PT has been extended by developing a framework of
ecient data management for the visualization of spatial data on mobile devices.
A data generalization framework is proposed and implemented in a software
application. This application can signicantly reduce the volume of data for
transmission and enable quick access to a simplied version of data while preserving
appropriate visualization quality. Using volunteered geographic information
as a case-study, the framework shows
exibility in delivering up-to-date spatial
information from dynamic data sources.
Three models of PT are designed and implemented to transmit the additional
LoD renements: a full scale PT as an inverse of generalisation, a viewdependent
PT, and a heuristic optimised view-dependent PT. These models are
evaluated with user trials and application examples. The heuristic optimised
view-dependent PT has shown a signicant enhancement over the traditional PT
in terms of bandwidth-saving and smoothness of transitions.
A parallel data management strategy associated with three corresponding
algorithms has been developed to handle LoD spatial data on mobile clients.
This strategy enables the map rendering to be performed in parallel with a process
which retrieves the data for the next map location the user will require. A viewdependent
approach has been integrated to monitor the volume of each LoD for
visible area. The demonstration of a
exible rendering style shows its potential
use in visualizing dynamic geoprocessed data. Future work may extend this
to integrate topological constraints and semantic constraints for enhancing the
vector map visualization
Statistical Feature Selection and Extraction for Video and Image Segmentation
The purpose of this study was to develop statistical feature selection and extraction methods for video and image segmentation, which partition a video or image into non-overlap and meaningful objects or regions. It is a fundamental step towards content-based visual information analysis. Visual data segmentation is a difficult task due to the various definitions of meaningful entities, as well as their complex properties and behaviors. Generally, visual data segmentation is a pattern recognition problem, where feature selection/extraction and data classifier design are two key components. Pixel intensity, color, time, texture, spatial location, shape, motion information, etc., are most frequently used features for visual data representation. Since not all of features are representative regarding visual data, and have significant contribution to the data classification, feature selection and/or extraction are necessary to select or generate salient features for data classifier. Statistical machine learning methods play important roles in developing data classifiers. In this report, both parametric and nonparametric machine learning methods are studied under three specific applications: video and image segmentation, as well as remote sensing data analysis. For various visual data segmentation tasks, key-frame extraction in video segmentation, WDHMM likelihood computation, decision tree training, and support vector learning are studied for feature selection and/or extraction and segmentation. Simulations on both synthetic and real data show that the proposed methods can provide accurate and robust segmentation results, as well as representative and discriminative features sets. This work can further inspire our studies towards the real applications. In these applications, we are able to obtain state-of-the-art or promising results as well as efficient algorithmsElectrical Engineering Technolog
Remote Sensing Image Scene Classification: Benchmark and State of the Art
Remote sensing image scene classification plays an important role in a wide
range of applications and hence has been receiving remarkable attention. During
the past years, significant efforts have been made to develop various datasets
or present a variety of approaches for scene classification from remote sensing
images. However, a systematic review of the literature concerning datasets and
methods for scene classification is still lacking. In addition, almost all
existing datasets have a number of limitations, including the small scale of
scene classes and the image numbers, the lack of image variations and
diversity, and the saturation of accuracy. These limitations severely limit the
development of new approaches especially deep learning-based methods. This
paper first provides a comprehensive review of the recent progress. Then, we
propose a large-scale dataset, termed "NWPU-RESISC45", which is a publicly
available benchmark for REmote Sensing Image Scene Classification (RESISC),
created by Northwestern Polytechnical University (NWPU). This dataset contains
31,500 images, covering 45 scene classes with 700 images in each class. The
proposed NWPU-RESISC45 (i) is large-scale on the scene classes and the total
image number, (ii) holds big variations in translation, spatial resolution,
viewpoint, object pose, illumination, background, and occlusion, and (iii) has
high within-class diversity and between-class similarity. The creation of this
dataset will enable the community to develop and evaluate various data-driven
algorithms. Finally, several representative methods are evaluated using the
proposed dataset and the results are reported as a useful baseline for future
research.Comment: This manuscript is the accepted version for Proceedings of the IEE
KOLAM : human computer interfaces fro visual analytics in big data imagery
In the present day, we are faced with a deluge of disparate and dynamic information from multiple heterogeneous sources. Among these are the big data imagery datasets that are rapidly being generated via mature acquisition methods in the geospatial, surveillance (specifically, Wide Area Motion Imagery or WAMI) and biomedical domains. The need to interactively visualize these imagery datasets by using multiple types of views (as needed) into the data is common to these domains. Furthermore, researchers in each domain have additional needs: users of WAMI datasets also need to interactively track objects of interest using algorithms of their choice, visualize the resulting object trajectories and interactively edit these results as needed. While software tools that fulfill each of these requirements individually are available and well-used at present, there is still a need for tools that can combine the desired aspects of visualization, human computer interaction (HCI), data analysis, data management, and (geo-)spatial and temporal data processing into a single flexible and extensible system. KOLAM is an open, cross-platform, interoperable, scalable and extensible framework for visualization and analysis that we have developed to fulfil the above needs. The novel contributions in this thesis are the following: 1) Spatio-temporal caching for animating both giga-pixel and Full Motion Video (FMV) imagery, 2) Human computer interfaces purposefully designed to accommodate big data visualization, 3) Human-in-the-loop interactive video object tracking - ground-truthing of moving objects in wide area imagery using algorithm assisted human-in-the-loop coupled tracking, 4) Coordinated visualization using stacked layers, side-by-side layers/video sub-windows and embedded imagery, 5) Efficient one-click manual tracking, editing and data management of trajectories, 6) Efficient labeling of image segmentation regions and passing these results to desired modules, 7) Visualization of image processing results generated by non-interactive operators using layers, 8) Extension of interactive imagery and trajectory visualization to multi-monitor wall display environments, 9) Geospatial applications: Providing rapid roam, zoom and hyper-jump spatial operations, interactive blending, colormap and histogram enhancement, spherical projection and terrain maps, 10) Biomedical applications: Visualization and target tracking of cell motility in time-lapse cell imagery, collecting ground-truth from experts on whole-slide imagery (WSI) for developing histopathology analytic algorithms and computer-aided diagnosis for cancer grading, and easy-to-use tissue annotation features.Includes bibliographical reference
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