Grid Services and Satellite Image Processing for Urban and River Bed Changes Assessment

2008 ◽  
Author(s):  
Cristina Gherghina ◽  
Eliza Isbasoiu
Keyword(s):  
Image Processing ◽  
Satellite Image ◽  
Grid Services ◽  
River Bed ◽  
Satellite Image Processing ◽  
Bed Changes

scholarly journals Multispectral inverse problems in satellite image processing

1998 ◽  
Author(s):  
Scott A. Starks ◽  
Vladik Kreinovich
Keyword(s):  
Image Processing ◽  
Inverse Problems ◽  
Satellite Image ◽  
Satellite Image Processing

scholarly journals Satellite Image Processing for Land Use and Land Cover Mapping

2014 ◽  
Vol 6 (10) ◽  
pp. 18-28 ◽  
Author(s):  
Ashoka Vanjare ◽  
S.N. Omkar ◽  
J. Senthilnath
Keyword(s):  
Image Processing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Image ◽  
Land Cover Mapping ◽  
Satellite Image Processing

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 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.

Assessing land-cover change and degradation in the Central Asian deserts using satellite image processing and geostatistical methods

2008 ◽  
Vol 72 (11) ◽  
pp. 2093-2105 ◽  
Author(s):  
A. Karnieli ◽  
U. Gilad ◽  
M. Ponzet ◽  
T. Svoray ◽  
R. Mirzadinov ◽  
...  
Keyword(s):  
Image Processing ◽  
Land Cover ◽  
Land Cover Change ◽  
Satellite Image ◽  
Geostatistical Methods ◽  
Central Asian ◽  
Satellite Image Processing

Multispectral Satellite Image Processing

2016 ◽  
pp. 57-124 ◽  
Author(s):  
Kenji Ose ◽  
Thomas Corpetti ◽  
Laurent Demagistri
Keyword(s):  
Image Processing ◽  
Satellite Image ◽  
Satellite Image Processing ◽  
Multispectral Satellite Image

Satellite Image Processing for Retrieving Historical Solar Irradiance Data Within the Mexican Territory

2019 ◽  
pp. 117-126 ◽  
Author(s):  
Juan M. Callejas-Cornejo ◽  
Manuel I. Peña-Cruz ◽  
Luis M. Valentín-Coronado
Keyword(s):  
Image Processing ◽  
Solar Irradiance ◽  
Satellite Image ◽  
Satellite Image Processing ◽  
Irradiance Data

Satellite Image Processing using Spark on the HUPI Platform

2020 ◽  
pp. 173-190
Author(s):  
Vincent Moreno ◽  
Minh Tu Nguyen
Keyword(s):  
Image Processing ◽  
Satellite Image ◽  
Satellite Image Processing
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