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 Design of image processing technique in digital enhancement application

2015 ◽  
Vol 1 (8) ◽  
pp. 340
Author(s):  
Bhubneshwar Sharma ◽  
Jyoti Dadwal
Keyword(s):  
Image Processing ◽  
Land Cover ◽  
Information Extraction ◽  
Digital Image ◽  
Visual Analysis ◽  
Satellite Image ◽  
Image Data ◽  
Image Processing Technique ◽  
Geometric Distortion ◽  
Satellite Image Processing

This paper describes the basic technological aspects of Digital Image Processing with special reference to satellite image processing. Basically, all satellite image-processing operations can be grouped into three categories: Image Rectification and Restoration, Enhancement and Information Extraction. The former deals with initial processing of raw image data to correct for geometric distortion, to calibrate the data radio metrically and to eliminate noise present in the data. The enhancement procedures are applied to image data in order to effectively display the data for subsequent visual interpretation. It involves techniques for increasing the visual distinction between features in a scene. The objective of the information extraction operations is to replace visual analysis of the image data with quantitative techniques for automating the identification of features in a scene. This involves the analysis of multispectral image data and the application of statistically based decision rules for determining the land cover identity of each pixel in an image. The intent of classification process is to categorize all pixels in a digital image into one of several land cover classes or themes. This classified data may be used to produce thematic maps of the land cover present in an image.

scholarly journals UTILIZATION EOS PLATFORM AS CLOUD-BASED GIS TO ANALYZE VEGETATION GREENNESS IN CIREBON REGENCY, INDONESIA

2020 ◽  
Vol 3 (1) ◽  
pp. 1
Author(s):  
Moh. Dede ◽  
Millary Agung Widiawaty
Keyword(s):  
Image Processing ◽  
Spatial Data ◽  
Urban Areas ◽  
Satellite Image ◽  
Google Earth ◽  
Vegetation Greenness ◽  
Satellite Image Processing ◽  
Google Earth Engine ◽  
Beta Version ◽  
Selection Of

Cloud-Based GIS development has been increasing rapidly since the need for big computing for online spatial data. Besides Google Earth Engine, there is actually another Cloud-Based GIS with similar features namely EOS Platform. This study aims to determine the EOS Platform utilization as a Cloud-Based GIS to Analyze Vegetation Greenness in Cirebon Regency, Indonesia. The selection of research location based on the various phenomenon of development in the Cirebon Regency. Vegetation greenness analysis using the NDVI algorithm which available on EOS Processing and Landsat series images are obtained from Land Viewer. Changes in vegetation greenness were analyzed descriptively from NDVI values in two periods at each pixel in the same location. The results of the analysis with the EOS Platform show a decreasing vegetation greenness in the western and peri-urban areas caused by LULC changes. From this analysis, it is proven that EOS Platform can be used for effective and efficient satellite image processing. Even so, some EOS Platform products with BETA version status still show some obstacles related to integration between products.

scholarly journals THERMAL AND VISIBLE SATELLITE IMAGE FUSION USING WAVELET IN REMOTE SENSING AND SATELLITE IMAGE PROCESSING

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 11-15
Author(s):  
A. H. Ahrari ◽  
M. Kiavarz ◽  
M. Hasanlou ◽  
M. Marofi
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Image Fusion ◽  
Satellite Image ◽  
Thermal Infrared ◽  
Qualitative Assessment ◽  
Spectral Information ◽  
Visible Image ◽  
Qualitative Approaches ◽  
Satellite Image Processing

Multimodal remote sensing approach is based on merging different data in different portions of electromagnetic radiation that improves the accuracy in satellite image processing and interpretations. Remote Sensing Visible and thermal infrared bands independently contain valuable spatial and spectral information. Visible bands make enough information spatially and thermal makes more different radiometric and spectral information than visible. However low spatial resolution is the most important limitation in thermal infrared bands. Using satellite image fusion, it is possible to merge them as a single thermal image that contains high spectral and spatial information at the same time. The aim of this study is a performance assessment of thermal and visible image fusion quantitatively and qualitatively with wavelet transform and different filters. In this research, wavelet algorithm (Haar) and different decomposition filters (mean.linear,ma,min and rand) for thermal and panchromatic bands of Landast8 Satellite were applied as shortwave and longwave fusion method . Finally, quality assessment has been done with quantitative and qualitative approaches. Quantitative parameters such as Entropy, Standard Deviation, Cross Correlation, Q Factor and Mutual Information were used. For thermal and visible image fusion accuracy assessment, all parameters (quantitative and qualitative) must be analysed with respect to each other. Among all relevant statistical factors, correlation has the most meaningful result and similarity to the qualitative assessment. Results showed that mean and linear filters make better fused images against the other filters in Haar algorithm. Linear and mean filters have same performance and there is not any difference between their qualitative and quantitative results.

scholarly journals SATELLITE IMAGE PROCESSING ON A GRID-BASED PLATFORM

2014 ◽  
pp. 51-58
Author(s):  
Dana Petcu ◽  
Dorian Gorgan ◽  
Florin Pop ◽  
Dacian Tudor ◽  
Daniela Zaharie
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Forest Fires ◽  
Satellite Image ◽  
Remote Sensing Image ◽  
Environmental Problems ◽  
Huge Amount ◽  
Satellite Image Processing ◽  
Remote Sensing Image Processing ◽  
Grid Based

Satellite image processing is both data and computing intensive, and, therefore, it raises several difficulties or even impossibilities while being using one single computer. Moreover, the analysis and sharing of the huge amount of data provided daily by the space satellites is a major challenge for the remote sensing community. Recently, Gridbased platforms were built to address these issues. This paper presents a specialized Grid-based platform developed to enable remote sensing image processing for environmental problems, like preventing river floods or forest fires. Moreover, it exposes the novelty elements that distinguish it from other similar approaches.

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