Cloud Computing in Remote Sensing

Lizhe Wang, Jining Yan, Yan Ma

• Publisher: Chapman and Hall/CRC
• Year: 2019
• ISBN:
  9780429949876
• Edition: 1
• Topic: Informatique
• Subtopic: Sciences générales de l'informatique

This book provides the users with quick and easy data acquisition, processing, storage and product generation services. It describes the entire life cycle of remote sensing data and builds an entire high performance remote sensing data processing system framework. It also develops a series of remote sensing data management and processing standards.

Features:

• Covers remote sensing cloud computing
• Covers remote sensing data integration across distributed data centers
• Covers cloud storage based remote sensing data share service
• Covers high performance remote sensing data processing
• Covers distributed remote sensing products analysis

Chapter 1

Remote Sensing and Cloud Computing

1.1Remote Sensing

1.1.1Remote sensing definition

1.1.2Remote sensing big data

1.1.3Applications of remote sensing big data

1.1.4Challenges of remote sensing big data

1.1.4.1Data integration challenges

1.1.4.2Data processing challenges

1.2Cloud Computing

1.2.1Cloud service models

1.2.2Cloud deployment models

1.2.3Security in the Cloud

1.2.4Open-source Cloud frameworks

1.2.4.1OpenStack

1.2.4.2Apache CloudStack

1.2.4.3OpenNebula

1.2.5Big data in the Cloud

1.2.5.1Big data management in the Cloud

1.2.5.2Big data analytics in the Cloud

1.3Cloud Computing in Remote Sensing

1.1 Remote Sensing

Remote sensing is a new comprehensive detection technology in the 1960s. Remote sensing technology, characterized by digital imaging, is an important symbol to measure a country’s scientific and technological development level and comprehensive strength.

1.1.1 Remote sensing definition

Remote sensing is generally defined as the technology of measuring the characteristics of an object or surface from a distance [1, 2]. It is the acquisition of information about an object or phenomenon without making physical contact with the object and thus stands in contrast to on-site observation. This generally refers to the use of sensors or remote sensors to detect the electromagnetic radiation and reflection characteristics of objects. The remote sensing technology acquires electromagnetic wave information (such as electric field, magnetic field, electromagnetic wave, seismic wave, etc.) that is reflected, radiated or scattered, and performs scientific techniques of extraction, measurement, processing, analysis and application.

At present, the term “remote sensing” generally refers to the detection and classification of objects on the earth, including the surface, the atmosphere and the oceans, based on transmitted signals (such as electromagnetic radiation), using sensor technology on satellites or aircraft [3]. It is also divided into “active” remote sensing and “passive” remote sensing.

1.1.2 Remote sensing big data

With the rapid development of remote sensing technology, our ability to obtain remote sensing data has been improved to an unprecedented level. We have entered an era of big data. Remote sensing data clear showing the characteristics of Big Data.

(1) Big volume

According to incomplete statistics, the total amount of data archived by the Earth Observing System Data and Information System (EOSDIS) reached 23.8 petabytes (PBs) around the year 2017, and kept 15.3 TB/day average archive growth speed. Up until October 2018, the archived data volume of the China National Satellite Meteorological Center (NSMC) reached 4.126 PBs, and the China Center for Resources Satellite Data and Application (CCRSDA) archived more than 16 PBs of remote sensing images until the end of 2016.

(2) Big variety

According to the 2017 state of the satellite industry report, there were 277 earth observation satellites in orbit by the end of 2016. These satellites all carried more than one earth observation sensor, and they can collect earth surface information continuously, day and night. That is to say, at least 277 kinds of earth observation data will be continuously transmitted to the ground receiving station. In addition, since Landsat-1 first started to deliver volumes of pixels in 1972, there have been more than 500 earth observation satellites launched into space, with archived remote sensing data of more than 1000 types.

(3) Big velocity

With the development of multi-satellite coordination and satellite constellation combination observation technologies, the satellite revisit periods gradually transition from month to day, hour or even minute. For example, the Jilin-1 satellite constellation consisting of 60 satellites will have a revisit cycle of 20 minutes by the end of 2020. In addition, the remote sensing data received by each data center arrives continuously at an ever-faster code rate. For example, the amount of data received from a GF-2 satellite PSM1 sensor is approximately 1.5 TB per day. It is preferable to ingest and archive the newly received data in order to provide users with the latest data retrieval and distribution service.

(4) Big value but small density

With spatial resolution and spectral resolution increases, more and finer ground information features could be captured by satellite sensors. Using remote sensing images, we can investigate Land-Use and Land-Cover Change (LUCC), monitor vegetation growth, discover ground military targets, etc. However, in order to obtain such valuable information, we have to process massive remote sensing images, just like picking up gold from the sand.

(5) Heterogeneous

Due to various satellite orbit parameters and the specifications of different sensors, the storage formats, projections, spatial resolutions, and revisit periods of the archived data are vastly different, and these differences have resulted in great difficulties for data collection. For example, Landsat 8 collects images of the Earth with a 16-day repeat cycle, referenced to Worldwide Reference System-2. The spatial resolution of the Operational Land Imager (OLI) sensor onboard the Landsat 8 satellite is about 30 meters; its collected images are stored in GeoTIFF format, with Hierarchical Data Format Earth Observation System (HDF-EOS) metadata. The Moderate Resolution Imaging Spectroradiometer (MODIS) instruments capture data in 36 spectral bands ranging in wavelength from 0.4 µm to 14.4 µm and at varying spatial resolutions (2 bands at 250 m, 5 bands at 500 m, and 29 bands at 1 km). Most of the MODIS data is available in the HDF-EOS format, and it is updated every 1 to 2 days. The Charge Coupled Device (CCD) sensor, which is carried by the Huan Jing (HJ)-1 mini satellite constellation, has an image swath of about 360 km, with blue, green, red, and NIR bands, 30m ground pixel resolution, and a 4-day revisit period. Its collected images are stored in GeoTIFF format, and their customized metadata is in eXtensible Markup Language (XML) format.

(6) Offsite storage

Generally speaking, different types of remote sensing data sources are stored in different satellite data centers, such as wind and cloud satellite data stored in meteorological satellite centers, and marine remote sensing data stored in marine satellite centers. In order to maximize the use of these earth observation and earth exploration data to serve us, we need to collect the offsite stored big earth data for unified management.

1.1.3 Applications of remote sensing big data

Now, remote sensing big data are attracting more and more attention from government projects and commercial applications to academic fields. It has been widely used in agriculture, disaster prevention and reduction, environmental monitoring, public safety and urban planning and other major macro application decisions, because of its advantages of obtaining large-scale and multi-angle geographic information [4].

In March 2012, the United States government proposed the “Big Data” Initiative. It could be the first government project on big data that focuses on improving our ability to extract knowledge from large and complex collections of digital data. For remote sensing big data, one of the most important US government projects is the Earth Observing System Data and Information System (EOSDIS). It provides end-to-end capabilities for managing NASA’s Earth science data from various sources. In Europe, the “Big Data from Space” conference was organized by the European Space Agency in 2017. It is to stimulate interactions and bring together researchers, engineers, users, infrastructure and service providers interested in exploiting big data from Space. In October 2017, the group on Earth Observations (GEO), the largest intergovernment multi-lateral cooperation organization, promotes the development of big data. Besides, the National GEOSS Data Sharing Platform of China is also delivered in the GEO Week 2017.

In the field of commercial applications, Google Earth could be one of the examples of success of remote sensing big data. Many remote sensing applications such as target detection, land-cover, smart city, etc. can be developed easily based on Google Earth. With the Digital Globes Geospatial Big Data platform (GBDX) ecosystem, the Digital Global company (Longmont, CO, USA) is building footprints quickly by leveraging machine learning in combination with Digital Globes cloud-based 100 petabyte imagery library. Other large companies such as Microsoft (Redmond, WA, USA) and Baidu (Beijing, China) are all developing their el...

Table of contents

• Cover
• Half Title
• Title Page
• Copyright Page
• Contents
• Preface
• 1. Remote Sensing and Cloud Computing
• 2. Remote Sensing Data Integration in a Cloud Computing Environment
• 3. Remote Sensing Data Organization and Management in a Cloud Computing Environment
• 4. High Performance Remote Sensing Data Processing in a Cloud Computing Environment
• 5. Programming Technologies for High Performance Remote Sensing Data Processing in a Cloud Computing Environment
• 6. Construction and Management of Remote Sensing Production Infrastructures across Multiple Satellite Data Centers
• 7. Remote Sensing Product Production in an OpenStack-Based Cloud Computing Environment
• 8. Knowledge Discovery and Information Analysis from Remote Sensing Big Data
• 9. Automatic Construction of Cloud Computing Infrastructures in Remote Sensing
• 10. Security Management in a Remote-Sensing-Oriented Cloud Computing Environment
• 11. A Cloud-Based Remote Sensing Information Service System Design and Implementation
• Bibliography
• Index