June 18, 2021

Download Ebook Free Robust Satellite Techniques For Natural And Man-made Hazards

Robust Satellite Techniques for Natural and Man-Made Hazards

Robust Satellite Techniques for Natural and Man-Made Hazards
Author : Valerio Tramutoli,Nicola Pergola
Publisher : Elsevier
Release Date : 2019-01-15
Category :
Total pages :400
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Robust Satellite Techniques for Natural and Man-made Hazards provides an introduction the Robust Satellite Techniques (RST) change detection method. This method is used to identify significant signal changes in a reliable way even in the presence of varying disturbing conditions, as they apply to natural, environmental, and industrial hazards. Providing both general and specific examples for the use of RST, Robust Satellite Techniques for Natural and Man-made Hazards offers a variety of applications for these techniques, spanning from natural hazard detection and environmental monitoring to industrial accident and terrorist attach early identification. Applicable to researchers, students, and policy makers alike in a variety of fields including Earth Sciences, Environmental Monitoring, and disaster risk reduction, this book is essential for understanding advanced applications and analyses of remote sensing data. Introduces a unique method to reliably detect hazards Addresses natural, environmental, and industrial hazards all in one compact resource Presents concepts in plain language to aid in multidisciplinary research and includes an appendix for non-experts in remote sensing Includes case studies and testing datasets with RST algorithms to reinforce concepts

Remote Sensing from Space

Remote Sensing from Space
Author : Bhupendra Jasani,Martino Pesaresi,Stefan Schneiderbauer,Gunter Zeug
Publisher : Springer Science & Business Media
Release Date : 2009-09-17
Category : Technology & Engineering
Total pages :297
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David Stevens Space-based information, which includes earth observation data, is increasingly becoming an integral part of our lives. We have been relying for decades on data obtained from meteorological satellites for updates on the weather and to monitor weather-related natural disasters such as hurricanes. We now count on our personal satellite-based navigation systems to guide us to the nearest Starbucks Coffee and use web-based applications such as Google Earth and Microsoft Virtual Earth to study the area of places we will or would like to visit. At the same time, satellite-based technologies have experienced impressive growth in recent years with an increase in the number of available sensors, an increase in spatial, temporal and spectral resolutions, an increase in the availability of radar satellites such as Terrasar-X and ALOS, and the launching of specific constellations such as the Disaster Monitoring Constellation (DMC), COSMO- SkyMed (COnstellation of small Satellites for the Mediterranean basin Observation) and RapidEye. Even more recent are the initiatives being set-up to ensure that space-based information is being accessed and used by decision makers, such as Sentinel Asia for the Asia and Pacific region and SERVIR for the Latin America and Caribbean region.

Advanced Methods for IDP and Refugee Camp Mapping with Very High Resolution Satellite Imagery

Advanced Methods for IDP and Refugee Camp Mapping with Very High Resolution Satellite Imagery
Author : Anonim
Publisher : Unknown
Release Date : 2008
Category :
Total pages :129
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Due to natural and man-made disasters, worldwide millions of people are seeking shelter in camps. Most of these camps are under supervision of national and international relief organizations which are responsible for supplying the camps with basic necessities. Therefore, knowing the number of inhabitants is an essential aspect for the effective management of the logistics. This study aims to show that very high resolution satellite imagery can be used for counting residential numbers by extractions of actual shelters in the camps. Based on recent publications, methods for shelter extraction were examined and modified. With images derived from IKONOS (1 m resolution) of two Refugee/IDP (Internally Displaced People) camps in eastern Chad a visual interpretation, three pixel-based approaches and one object-based analysis were tested. Additionally, a visual interpretation of images recorded by the new radar satellite TerraSAR-X was applied to show the possibilities for refugee camp mapping with spatially very high resolution radar. In this context, IKONOS and TerraSAR-X images were combined. The pixel and object-based methods for shelter extraction were discussed with regard to their accuracy and time expenditure as well as the possibilities for their implementation in diverse crisis situations. Therefore an exhaustive accuracy assessment on users, producers and overall accuracy was performed. Moreover, the different characteristics of the images were discussed. It was shown that the overall accuracy of the different methods ranges from about 34% to 88%. This discrepancy is due to characteristics of the shelters in the two campsites and the diverse methods for extraction. The object-based approach turned out to be the most robust technique with regard to the transferability to other imagery. The combined method of IKONOS and TerraSAR-X showed a high potential for more precise techniques in shelter mapping for future approaches.

Earth Observing System: From pattern to process: the strategy of the earth observing system

Earth Observing System: From pattern to process: the strategy of the earth observing system
Author : Anonim
Publisher : Unknown
Release Date : 1984
Category : Remote sensing
Total pages :129
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Earth Observing System

Earth Observing System
Author : Anonim
Publisher : Unknown
Release Date : 1987
Category : Earth resources technology satellites
Total pages :140
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Automatic Extraction of Man-made Objects from Aerial and Satellite Images III

Automatic Extraction of Man-made Objects from Aerial and Satellite Images III
Author : E.P. Baltsavias,A. Gruen,L. VanGool
Publisher : CRC Press
Release Date : 2001-01-01
Category : Computers
Total pages :425
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This work is a collection of papers from the world's leading research groups in the field of automatic extraction of objects, especially buildings and roads, from aerial and space imagery, including new sensors like SAR and lidar.

Recent Developments in Remote Sensing for Human Disaster Management and Mitigation Natural and Man-Made (2013)

Recent Developments in Remote Sensing for Human Disaster Management and Mitigation Natural and Man-Made (2013)
Author : Christopher Lavers
Publisher : Lulu.com
Release Date : 2021
Category :
Total pages :129
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International Aerospace Abstracts

International Aerospace Abstracts
Author : Anonim
Publisher : Unknown
Release Date : 1999
Category : Aeronautics
Total pages :129
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Understanding High Resolution Aerial Imagery Using Computer Vision Techniques

Understanding High Resolution Aerial Imagery Using Computer Vision Techniques
Author : Fan Wang
Publisher : Unknown
Release Date : 2017
Category : Computer vision
Total pages :182
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"Computer vision can make important contributions to the analysis of remote sensing satellite or aerial imagery. However, the resolution of early satellite imagery was not sufficient to provide useful spatial features. The situation is changing with the advent of very-high-spatial-resolution (VHR) imaging sensors. This change makes it possible to use computer vision techniques to perform analysis of man-made structures. Meanwhile, the development of multi-view imaging techniques allows the generation of accurate point clouds as ancillary knowledge. This dissertation aims at developing computer vision and machine learning algorithms for high resolution aerial imagery analysis in the context of application problems including debris detection, building detection and roof condition assessment. High resolution aerial imagery and point clouds were provided by Pictometry International for this study. Debris detection after natural disasters such as tornadoes, hurricanes or tsunamis, is needed for effective debris removal and allocation of limited resources. Significant advances in aerial image acquisition have greatly enabled the possibilities for rapid and automated detection of debris. In this dissertation, a robust debris detection algorithm is proposed. Large scale aerial images are partitioned into homogeneous regions by interactive segmentation. Debris areas are identified based on extracted texture features. Robust building detection is another important part of high resolution aerial imagery understanding. This dissertation develops a 3D scene classification algorithm for building detection using point clouds derived from multi-view imagery. Point clouds are divided into point clusters using Euclidean clustering. Individual point clusters are identified based on extracted spectral and 3D structural features. The inspection of roof condition is an important step in damage claim processing in the insurance industry. Automated roof condition assessment from remotely sensed images is proposed in this dissertation. Initially, texture classification and a bag-of-words model were applied to assess the roof condition using features derived from the whole rooftop. However, considering the complexity of residential rooftop, a more sophisticated method is proposed to divide the task into two stages: 1) roof segmentation, followed by 2) classification of segmented roof regions. Deep learning techniques are investigated for both segmentation and classification. A deep learned feature is proposed and applied in a region merging segmentation algorithm. A fine-tuned deep network is adopted for roof segment classification and found to achieve higher accuracy than traditional methods using hand-crafted features. Contributions of this study include the development of algorithms for debris detection using 2D images and building detection using 3D point clouds. For roof condition assessment, the solutions to this problem are explored in two directions: features derived from the whole rooftop and features extracted from each roof segments. Through our research, roof segmentation followed by segments classification was found to be a more promising method and the workflow processing developed and tested. Deep learning techniques are also investigated for both roof segmentation and segments classification. More unsupervised feature extraction techniques using deep learning can be explored in future work."--Abstract.

AGU 2004 Joint Assembly

AGU 2004 Joint Assembly
Author : American Geophysical Union. Joint Assembly
Publisher : Unknown
Release Date : 2004
Category : Geochemistry
Total pages :542
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Government Reports Announcements & Index

Government Reports Announcements & Index
Author : Anonim
Publisher : Unknown
Release Date : 1995
Category : Science
Total pages :129
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Spring Meeting

Spring Meeting
Author : American Geophysical Union. Meeting
Publisher : Unknown
Release Date : 2001
Category : Geophysics
Total pages :129
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Annual Report

Annual Report
Author : Rand Corporation
Publisher : Unknown
Release Date : 2002
Category :
Total pages :129
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Petroleum Abstracts

Petroleum Abstracts
Author : Anonim
Publisher : Unknown
Release Date : 1992-04
Category : Petroleum
Total pages :129
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Applications and Science of Artificial Neural Networks

Applications and Science of Artificial Neural Networks
Author : Anonim
Publisher : Unknown
Release Date : 1995
Category : Neural networks (Computer science)
Total pages :129
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Volumes consist of the proceedings of the International Conference on Applications and Science of Artificial Neural Networks.