Big Data for Satellite Image Processing

2022 ◽  
pp. 1110-1124
Author(s):  
Remya S. ◽  
Ramasubbareddy Somula ◽  
Sravani Nalluri ◽  
Vaishali R. ◽  
Sasikala R.
Keyword(s):  
Image Processing ◽  
Big Data ◽  
Satellite Image ◽  
High Volume ◽  
The Other ◽  
Essential Information ◽  
Satellite Image Processing ◽  
Architecture Modeling

This chapter presents an introduction to the basics in big data including architecture, modeling, and the tools used. Big data is a term that is used for serving the high volume of data that can be used as an alternative to RDBMS and the other analytical technologies such as OLAP. For every application there exist databases that contain the essential information. But the sizes of the databases vary in different applications and we need to store, extract, and modify these databases. In order to make it useful, we have to deal with it efficiently. This is the place that big data plays an important role. Big data exceeds the processing and the overall capacity of other traditional databases. In this chapter, the basic architecture, tools, modeling, and challenges are presented in each section.

Big Data for Satellite Image Processing

2018 ◽  
pp. 133-150
Author(s):  
Remya S. ◽  
Ramasubbareddy Somula ◽  
Sravani Nalluri ◽  
Vaishali R. ◽  
Sasikala R.
Keyword(s):  
Image Processing ◽  
Big Data ◽  
Satellite Image ◽  
High Volume ◽  
The Other ◽  
Essential Information ◽  
Satellite Image Processing ◽  
Architecture Modeling

This chapter presents an introduction to the basics in big data including architecture, modeling, and the tools used. Big data is a term that is used for serving the high volume of data that can be used as an alternative to RDBMS and the other analytical technologies such as OLAP. For every application there exist databases that contain the essential information. But the sizes of the databases vary in different applications and we need to store, extract, and modify these databases. In order to make it useful, we have to deal with it efficiently. This is the place that big data plays an important role. Big data exceeds the processing and the overall capacity of other traditional databases. In this chapter, the basic architecture, tools, modeling, and challenges are presented in each section.

scholarly journals Optical Remote Sensing

2021 ◽  
pp. 315-344
Author(s):  
Man Sing Wong ◽  
Xiaolin Zhu ◽  
Sawaid Abbas ◽  
Coco Yin Tung Kwok ◽  
Meilian Wang
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Information Extraction ◽  
Urban Areas ◽  
Satellite Image ◽  
Background Information ◽  
Optical Remote Sensing ◽  
Basic Operation ◽  
City Development ◽  
Satellite Image Processing

AbstractApplications of Earth-observational remote sensing are rapidly increasing over urban areas. The latest regime shift from conventional urban development to smart-city development has triggered a rise in smart innovative technologies to complement spatial and temporal information in new urban design models. Remote sensing-based Earth-observations provide critical information to close the gaps between real and virtual models of urban developments. Remote sensing, itself, has rapidly evolved since the launch of the first Earth-observation satellite, Landsat, in 1972. Technological advancements over the years have gradually improved the ground resolution of satellite images, from 80 m in the 1970s to 0.3 m in the 2020s. Apart from the ground resolution, improvements have been made in many other aspects of satellite remote sensing. Also, the method and techniques of information extraction have advanced. However, to understand the latest developments and scope of information extraction, it is important to understand background information and major techniques of image processing. This chapter briefly describes the history of optical remote sensing, the basic operation of satellite image processing, advanced methods of object extraction for modern urban designs, various applications of remote sensing in urban or peri-urban settings, and future satellite missions and directions of urban remote sensing.

scholarly journals The significance of typography in data visualization

2021 ◽  
Vol 11 (1) ◽  
pp. 12-23
Author(s):  
Halime Turkkan
Keyword(s):  
Big Data ◽  
Data Visualization ◽  
Graphic Design ◽  
Design Space ◽  
Critical Issue ◽  
Information Design ◽  
The Other ◽  
Essential Information ◽  
Digital World ◽  
Design Systems

With the development of technology and the dominance of the digital world, typography has become a critical issue. Information design systems are considered as one of the significant areas of graphic design and big data provides essential information on data visualization. This research aims to analyse the effects of typographic elements on visualizing data in terms of visual communication, by discussing the value that typography gives to design space. The research discusses randomly selected 10 infographic design samples published in the last six months on google. From the results, 5 designs with typographic concern were more favourable and visually more striking and preferable than the other 5 designs in terms of design disciplines. As in all areas of graphic design, it is argued that the power of typography is an indisputable concept in data visualization, which is seen as a sub-branch of information design. Keywords: data visualization; design; typography, significance, technology

scholarly journals Analysis of Changes in Area and Density of Mangroves through Landsat 8 Satellite Image Processing

2019 ◽  
Vol 9 (2) ◽  
pp. 16-22
Author(s):  
Nadya Fiqi Nurcahyani
Keyword(s):  
Image Processing ◽  
Satellite Imagery ◽  
Good Accuracy ◽  
Social Values ◽  
Satellite Image ◽  
Landsat 8 ◽  
Mangrove Forests ◽  
Android Application ◽  
Mangrove Area ◽  
Satellite Image Processing

Mangrove forests have high ecological, economic and social values ??which function to maintain shoreline stability, protect beaches and riverbanks, filter and remediate waste, and to withstand floods and waves. The facts show that mangrove damage is everywhere, even the intensity of damage and its area tends to increase significantly. Many roles of mangroves require proper management to maintain the existence of mangroves. One way to determine the area of ??mangroves is by processing Landsat 8 satellite imagery. The stages of mangrove identification are carried out by using 564 RGB band merger, then separating the mangrove and non-mangrove objects. Next step is to analyze the density of mangroves using NDVI formula. To maximize monitoring of mangrove area, an android application was created that provides information on the area and density of mangroves at several locations, namely Clungup, Bangsong Teluk Asmara and Cengkrong from 2015 to 2018.The results showed that Landsat 8 satellite imagery can be used to identify changes in the area of ??mangrove forests with good accuracy, namely in the Clungup area of ??90% and Cengkrong of 86.67%. From processing results, the mangrove area in the Clungup area has also decreased from 2015 to 2017 but has increased in 2018 so that the application provides recommendations for embroidering mangroves in 2016 to 2017 and mangrove recommendations are maintained in 2018. As for Bangsong Teluk area Asmara and Cengkrong have increased the area of ??mangroves every year so that the application provides recommendations to be maintained from 2016 to 2018.

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