recentdevelopments in the remotesensing systems and imageprocessing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth image...
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ISBN:
(数字)9781510627499
ISBN:
(纸本)9781510627499
recentdevelopments in the remotesensing systems and imageprocessing made it possible to propose a new method of the object classification and detection of the specific changes in the series of satellite Earth images ( so called targeted change detection). In this paper we develop a formal problem statement that allows to use effectively the deep learning approach to analyze time-dependent series of remotesensingimages. We also introduce a new framework for the development of deep learning models for targeted change detection and demonstrate some cases of business applications it can be used for.
Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images...
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ISBN:
(数字)9789811364242
ISBN:
(纸本)9789811364235
Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities, satellite image acquisition and mining are now on the rise. In turn, satellite images play a vital role in providing essential geographical information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error, and allow the rapid and rigorous analysis of land use and land cover information. Integrating Machine Learning (ML) technology with the human visual psychometric can help meet geologists' demands for more efficient and higher-quality classification in real time. This book introduces readers to key concepts, methods and models for satellite image analysis; highlights state-of-the-art classification and clustering techniques; discusses recentdevelopments and remaining challenges; and addresses various applications, making it a valuable asset for engineers, data analysts and researchers in the fields of geographic information systems and remotesensing engineering.
The recent scientific and technical developments of reverse engineering methods and tools have broadened the possibilities of applications in the field of cultural heritage conservation. In this paper, two different n...
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The recent scientific and technical developments of reverse engineering methods and tools have broadened the possibilities of applications in the field of cultural heritage conservation. In this paper, two different non-contact reverse engineering systems were utilized for 3D data acquisition of a cultural heritage artefact. The object of interest is a 17th century wooden engraved ecclesiastical sanctuary ciborium. The requirement of the 3D model is to aid the art conservators for the preservation of the wooden material and the restoration of small damages and cracks in the engraved parts, thus requiring accuracy of the model in the order of submillimetre. In this work, a Faro Vantage laser tracker was employed along with the FARO Edge Arm. In addition, image-based modelling was also implemented with a large number of overlapping images acquired with a Canon EOS 6D camera and processed using the well-known Structure from Motion (SfM) method with an auto-calibration procedure. The digital data acquisition and processing procedures of the scanned geometry are described and compared to evaluate the performance of both systems in terms of data acquisition time, processing time, reconstruction precision and final model quality. Whilst models produced with laser scanning and image-based techniques is not a novel approach, the combination of laser tracking and photogrammetric data still presents limited documentation in the field of cultural artefact documentation mainly due to the extremely high cost of the laser tracking systems.
developments in satellite technology, remote sensors and drone technologies are mushrooming. These developments yield volumes of high quality scene images that require effective processing for intelligent farming appl...
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ISBN:
(数字)9781905824656
ISBN:
(纸本)9781728157740
developments in satellite technology, remote sensors and drone technologies are mushrooming. These developments yield volumes of high quality scene images that require effective processing for intelligent farming applications. The recent deep learning technologies can leverage these opportunities to fuse computer vision and artificial intelligence in farming. This encompasses the big data phenomena and huge volumes of data that are captured, processed and applied for decision-making. This paper aims to give insights on the integration of computer vision for smart farming in-order to attain sustainable agriculture. Using a structured approach, this research proposes a computer vision technique for crop image feature characterization that applies in the determination of the crop's health status. To achieve this, a deep convolutional network is applied for image feature extraction and representation, and then these features are fed to the support vector-learning machine for training and subsequent image interpretation. From the experimental results, it is evident that the proposed technique generates superior visual interpretation results of scene images as compared to other methods in literature. It follows that the Global food security and agricultural sustainability can be attained through ICT enabled solutions that are integrates and works together a phenomenon referred to as smart farming.
Over the past decade, the remote-sensing community has eagerly adopted unmanned aircraft systems (UAS) as a cost-effective means to capture imagery at spatial and temporal resolutions not typically feasible with manne...
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Over the past decade, the remote-sensing community has eagerly adopted unmanned aircraft systems (UAS) as a cost-effective means to capture imagery at spatial and temporal resolutions not typically feasible with manned aircraft and satellites. The rapid adoption has outpaced our understanding of the relationships between data collection methods and data quality, causing uncertainties in data and products derived from UAS and necessitating exploration into how researchers are using UAS for terrestrial applications. We synthesize these procedures through a meta-analysis of UAS applications alongside a review of recent, basic science research surrounding theory and method development. We performed a search of the Web of Science (WoS) database on 17 May 2017 using UAS-related keywords to identify all peer-reviewed studies indexed by WoS. We manually filtered the results to retain only terrestrial studies () and further categorized results into basic theoretical studies (), method development (), and applications (). After randomly selecting a subset of applications (), we performed an in-depth content analysis to examine platforms, sensors, data capture parameters (e.g. flight altitude, spatial resolution, imagery overlap, etc.), preprocessing procedures (e.g. radiometric and geometric corrections), and analysis techniques. Our findings show considerable variation in UAS practices, suggesting a need for establishing standardized image collection and processing procedures. We reviewed basic research and methodological developments to assess how data quality and uncertainty issues are being addressed and found those findings are not necessarily being considered in application studies.
High spatial resolution remotesensing is an area of considerable current interest and builds on developments in object-based image analysis, commercial high-resolution satellite sensors, and UAVs. It captures more de...
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ISBN:
(数字)9780429893001
ISBN:
(纸本)9781498767682
High spatial resolution remotesensing is an area of considerable current interest and builds on developments in object-based image analysis, commercial high-resolution satellite sensors, and UAVs. It captures more details through high and very high resolution images (10 to 100 cm/pixel). This unprecedented level of detail offers the potential extraction of a range of multi-resource management information, such as precision farming, invasive and endangered vegetative species delineation, forest gap sizes and distribution, locations of highly valued habitats, or sub-canopy topographic information. Information extracted in high spatial remotesensing data right after a devastating earthquake can help assess the damage to roads and buildings and aid in emergency planning for contact and evacuation.
To effectively utilize information contained in high spatial resolution imagery, High Spatial Resolution remotesensing: Data, Analysis, and applications addresses some key questions:
What are the challenges of using new sensors and new platforms?
What are the cutting-edge methods for fine-level information extraction from high spatial resolution images?
How can high spatial resolution data improve the quantification and characterization of physical-environmental or human patterns and processes?
The answers are built in three separate parts: (1) data acquisition and preprocessing, (2) algorithms and techniques, and (3) case studies and applications. They discuss the opportunities and challenges of using new sensors and platforms and high spatial resolution remotesensing data and recentdevelopments with a focus on UAVs. This work addresses the issues related to high spatial imageprocessing and introduces cutting-edge methods, summarizes state-of-the-art high spatial resolution applications, and demonstrates how high spatial resolution remotesensing can support the extraction of detailed information needed in different systems. Using various high spatial resolutio
recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging b...
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recent technical advances in drones make them increasingly relevant and important tools for forest measurements. However, information on how to optimally set flight parameters and choose sensor resolution is lagging behind the technical developments. Our study aims to address this gap, exploring the effects of drone flight parameters (altitude, image overlap, and sensor resolution) on image reconstruction and successful 3D point extraction. This study was conducted using video footage obtained from flights at several altitudes, sampled for images at varying frequencies to obtain forward overlap ratios ranging between 91 and 99%. Artificial reduction of image resolution was used to simulate sensor resolutions between 0.3 and 8.3 Megapixels (Mpx). The resulting data matrix was analysed using commercial multi-view reconstruction (MVG) software to understand the effects of drone variables on (1) reconstruction detail and precision, (2) flight times of the drone, and (3) reconstruction times during data processing. The correlations between variables were statistically analysed with a multivariate generalised additive model (GAM), based on a tensor spline smoother to construct response surfaces. Flight time was linearly related to altitude, while processing time was mainly influenced by altitude and forward overlap, which in turn changed the number of images processed. Low flight altitudes yielded the highest reconstruction details and best precision, particularly in combination with high image overlaps. Interestingly, this effect was nonlinear and not directly related to increased sensor resolution at higher altitudes. We suggest that image geometry and high image frequency enable the MVG algorithm to identify more points on the silhouettes of tree crowns. Our results are some of the first estimates of reasonable value ranges for flight parameter selection for forestry applications.
The data center is a new concept of data processing and application proposed in recent years. It is a new method of processing technologies based on data, parallel computing, and compatibility with different hardware ...
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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face i...
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Advances in Unmanned Aerial Vehicles (UAVs), also known as drones, offer unprecedented opportunities to boost a wide array of large-scale Internet of Things (IoT) applications. Nevertheless, UAV platforms still face important limitations mainly related to autonomy and weight that impact their remotesensing capabilities when capturing and processing the data required for developing autonomous and robust real-time obstacle detection and avoidance systems. In this regard, Deep Learning (DL) techniques have arisen as a promising alternative for improving real-time obstacle detection and collision avoidance for highly autonomous UAVs. This article reviews the most recentdevelopments on DL Unmanned Aerial Systems (UASs) and provides a detailed explanation on the main DL techniques. Moreover, the latest DL-UAV communication architectures are studied and their most common hardware is analyzed. Furthermore, this article enumerates the most relevant open challenges for current DL-UAV solutions, thus allowing future researchers to define a roadmap for devising the new generation affordable autonomous DL-UAV IoT solutions.
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