The Differences And Similarities For Archiving Images From Medicine, Document Storage, Satellite Imagery, Seismic Exploration, Astronomy And General Image Processing

1984 ◽  
Author(s):  
Harold C. Rutherford
Keyword(s):  
Image Processing ◽  
Satellite Imagery ◽  
Seismic Exploration ◽  
Document Storage

Image processing for electronic document storage

1988 ◽  
Vol 135 (4) ◽  
pp. 196
Author(s):  
A. Cooper ◽  
W. Kahari ◽  
R. Such
Keyword(s):  
Image Processing ◽  
Electronic Document ◽  
Document Storage

scholarly journals Ground Data Inputs To Image Processing for Estimating Terrain Characteristics for Glacio-Hydrological Analysis

1987 ◽  
Vol 9 ◽  
pp. 45-49 ◽  
Author(s):  
M.J. Clark ◽  
A.M. Gurnell ◽  
P.J. Hancock
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Satellite Imagery ◽  
Geometric Correction ◽  
Data Set ◽  
Glacial Environments ◽  
Methodological Problems ◽  
Terrain Characteristics ◽  
Radiometric Data

Remote-sensing research in glacial and pro-glacial environments raises several methodological problems relating to the handling of ground and satellite radiometric data. An evaluation is undertaken of the use of ground radiometry to elucidate properties of relevant surface types in order to interpret satellite imagery. It identifies the influence that geometric correction and re-sampling have on the radiometric purity of the resulting data set. Methodological problems inherent in deriving catchment terrain characteristics are discussed with reference to currently glacierized and pro-glacial zones of south-western Switzerland.

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.

Remote Sensing of Broom Snakeweed (Gutierrezia Sarothrae) with Noaa-10 Spectral Image Processing

1992 ◽  
Vol 6 (4) ◽  
pp. 1015-1020 ◽  
Author(s):  
Albert J. Peters ◽  
Bradley C. Reed ◽  
Marlen D. Eve ◽  
Kirk C. McDaniel
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
New Mexico ◽  
Spatial Resolution ◽  
Satellite Imagery ◽  
Spectral Image ◽  
Late Season ◽  
Shortgrass Prairie ◽  
Gutierrezia Sarothrae ◽  
Meteorological Satellite

Low-spatial resolution satellite imagery from the NOAA-10 polar-orbiting meteorological satellite was analyzed to determine if central New Mexico grasslands infested by broom snakeweed could be discriminated from unaffected areas. Distinctive phenological characteristics of broom snakeweed, including an early season growth flush and late season flowering, enable moderate to heavily infested areas to be separated from grasslands having few or no weeds present. The procedure used shows promise as a tool for locating and monitoring brown snakeweed and other weeds growing on shortgrass prairie.

scholarly journals Automating Drone Image Processing to Map Coral Reef Substrates Using Google Earth Engine

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 50
Author(s):  
Mary K. Bennett ◽  
Nicolas Younes ◽  
Karen Joyce
Keyword(s):  
Image Processing ◽  
Coral Reef ◽  
High Resolution ◽  
Satellite Imagery ◽  
Coral Cover ◽  
Google Earth ◽  
Live Coral ◽  
Coral Sand ◽  
Google Earth Engine

While coral reef ecosystems hold immense biological, ecological, and economic value, frequent anthropogenic and environmental disturbances have caused these ecosystems to decline globally. Current coral reef monitoring methods include in situ surveys and analyzing remotely sensed data from satellites. However, in situ methods are often expensive and inconsistent in terms of time and space. High-resolution satellite imagery can also be expensive to acquire and subject to environmental conditions that conceal target features. High-resolution imagery gathered from remotely piloted aircraft systems (RPAS or drones) is an inexpensive alternative; however, processing drone imagery for analysis is time-consuming and complex. This study presents the first semi-automatic workflow for drone image processing with Google Earth Engine (GEE) and free and open source software (FOSS). With this workflow, we processed 230 drone images of Heron Reef, Australia and classified coral, sand, and rock/dead coral substrates with the Random Forest classifier. Our classification achieved an overall accuracy of 86% and mapped live coral cover with 92% accuracy. The presented methods enable efficient processing of drone imagery of any environment and can be useful when processing drone imagery for calibrating and validating satellite imagery.

scholarly journals Ground Data Inputs To Image Processing for Estimating Terrain Characteristics for Glacio-Hydrological Analysis

1987 ◽  
Vol 9 ◽  
pp. 45-49
Author(s):  
M.J. Clark ◽  
A.M. Gurnell ◽  
P.J. Hancock
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Satellite Imagery ◽  
Geometric Correction ◽  
Data Set ◽  
Glacial Environments ◽  
Methodological Problems ◽  
Terrain Characteristics ◽  
Radiometric Data

Remote-sensing research in glacial and pro-glacial environments raises several methodological problems relating to the handling of ground and satellite radiometric data. An evaluation is undertaken of the use of ground radiometry to elucidate properties of relevant surface types in order to interpret satellite imagery. It identifies the influence that geometric correction and re-sampling have on the radiometric purity of the resulting data set. Methodological problems inherent in deriving catchment terrain characteristics are discussed with reference to currently glacierized and pro-glacial zones of south-western Switzerland.

scholarly journals Detection of coffee berry necrosis by digital image processing of landsat 8 oli satellite imagery

2020 ◽  
Vol 85 ◽  
pp. 101983 ◽  
Author(s):  
Jonathan da Rocha Miranda ◽  
Marcelo de Carvalho Alves ◽  
Edson Ampélio Pozza ◽  
Helon Santos Neto
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Satellite Imagery ◽  
Digital Image ◽  
Landsat 8 ◽  
Landsat 8 Oli ◽  
Coffee Berry
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