A Review of Various Categories of Satellite Image Processing in Remote Sensing

2018 ◽  
pp. 325-333
Author(s):  
Rupinder Kaur ◽  
Dolly Sharma
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Satellite Image ◽  
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 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.

scholarly journals Categorizing and measurement satellite image processing of fire in the forest greece using remote sensing

2021 ◽  
Vol 21 (2) ◽  
pp. 846
Author(s):  
Ali Abdul Wahhab Mohammed ◽  
Hussein Thary Khamees
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
High Resolution ◽  
Satellite Images ◽  
Satellite Image ◽  
Before And After ◽  
Satellite Image Processing ◽  
Sentinel 2A ◽  
Grouping Algorithm

This paper has been utilized satellite Sentinel-2A imagery, this satellite is a polar-orbiting, multispectral high-resolution to cover Athens city, Greece that located at latitude (37° 58′ 46″) N, (23° 42′ 58″) E.,the work aims to measurement and study the wildfires natural resourcesbefore and after fire break out that happenedin forests of Athens city in Greece for a year (2007, 2018) and analysis the damage caused by these wildfiresand their impact on environment  and soil  by categorize the satellite images for the interested region before and after wildfires for a year (2007) and  a year (2018) and Discuss techniques that compute the area covered of each class and lessen  or limit the rapidly spreading wildfires damage.The categorizing utilizing the moments with (K-Means) grouping algorithm in RS (remote sensing). And the categorizing results show five unique classes (water, trees, buildings without tree, buildings with tree, bare lands) where, it can be notice that the region secured by each class before and after wildfires and the changed pixels for all classes.The experimental resulted of categorizing technique shows that the good performance exactness with a good categorizing and result analysisa bout the harms resulted from the fires in the forest Greece for a years (2007 and 2018).

RNN-based multispectral satellite image processing for remote sensing applications

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Venkata Dasu Marri ◽  
Veera Narayana Reddy P. ◽  
Chandra Mohan Reddy S.
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Deep Learning ◽  
Image Classification ◽  
Satellite Image ◽  
Multispectral Image ◽  
Content Type ◽  
Colliding Bodies Optimization ◽  
Sensing Applications ◽  
Remote Sensing Applications

Purpose Image classification is a fundamental form of digital image processing in which pixels are labeled into one of the object classes present in the image. Multispectral image classification is a challenging task due to complexities associated with the images captured by satellites. Accurate image classification is highly essential in remote sensing applications. However, existing machine learning and deep learning–based classification methods could not provide desired accuracy. The purpose of this paper is to classify the objects in the satellite image with greater accuracy. Design/methodology/approach This paper proposes a deep learning-based automated method for classifying multispectral images. The central issue of this work is that data sets collected from public databases are first divided into a number of patches and their features are extracted. The features extracted from patches are then concatenated before a classification method is used to classify the objects in the image. Findings The performance of proposed modified velocity-based colliding bodies optimization method is compared with existing methods in terms of type-1 measures such as sensitivity, specificity, accuracy, net present value, F1 Score and Matthews correlation coefficient and type 2 measures such as false discovery rate and false positive rate. The statistical results obtained from the proposed method show better performance than existing methods. Originality/value In this work, multispectral image classification accuracy is improved with an optimization algorithm called modified velocity-based colliding bodies optimization.

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