scholarly journals Satellite Image Processing Systems: An Architectural Perspective

2022 ◽  
pp. 116-121
Author(s):  
Ms. Puja V. Gawande ◽  
Dr. Sunil Kumar
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Satellite Image ◽  
Processing System ◽  
Big Data Applications ◽  
Complex Image ◽  
Satellite Image Processing ◽  
Prediction Systems ◽  
Architectural Perspective ◽  
Processing Steps

Satellite image processing systems include satellite image classification, long ranged data processing, yield prediction systems, etc. All of these systems require a large quantity of images for effective processing, and thus they are directed towards big-data applications. All these applications require a series of highly complex image processing and signal processing steps, which include but are not limited to image acquisition, image pre-processing, segmentation, feature extraction & selection, classification and post processing. Numerous researchers globally have proposed a large variety of algorithms, protocols and techniques in order to effectively process satellite images. This makes it very difficult for any satellite image system designer to develop a highly effective and application-oriented processing system. In this paper, we aim to categorize these large number of researches w.r.t. their effectiveness and further perform statistical analysis on the same. This study will assist researchers in selecting the best and most optimally performing algorithmic combinations in order to design a highly accurate satellite image processing system.

scholarly journals A WEB-BASED VIRTUAL LABORATORY FOR SATELLITE IMAGE PROCESSING AND ANALYSIS

2018 ◽  
Vol XLII-5 ◽  
pp. 9-13
Author(s):  
K. M. Buddhiraju ◽  
L. N. Eeti ◽  
K. K. Tiwari
Keyword(s):  
Image Processing ◽  
System Architecture ◽  
Satellite Images ◽  
Satellite Image ◽  
Virtual Laboratory ◽  
Web Based ◽  
Continuous Increase ◽  
Image Processing And Analysis ◽  
The World ◽  
Satellite Image Processing

<p><strong>Abstract.</strong> With continuous increase in the utilization of satellite images in various engineering and science fields, it is imperative to equip students with additional educational aid in subject of satellite image processing and analysis. In this paper a web-based virtual laboratory, which is accessible via internet to anyone around the world with no cost or constraints, is presented. Features of the laboratory has been discussed in addition to details regarding system architecture and its implementation. Virtual laboratory is tested by students, whose responses are also presented in this paper. Future development of this laboratory is outlined in the end.</p>

scholarly journals Image Processing Techniques for Analysis of Satellite Images for Historical Maps Classification—An Overview

2020 ◽  
Vol 10 (12) ◽  
pp. 4207 ◽  
Author(s):  
Anju Asokan ◽  
J. Anitha ◽  
Monica Ciobanu ◽  
Andrei Gabor ◽  
Antoanela Naaji ◽  
...  
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Satellite Image ◽  
Historical Maps ◽  
Satellite Imaging ◽  
Image Processing Techniques ◽  
Innovative Solutions ◽  
Satellite Image Processing ◽  
Processing Techniques ◽  
Selection Of

Historical maps classification has become an important application in today’s scenario of everchanging land boundaries. Historical map changes include the change in boundaries of cities/states, vegetation regions, water bodies and so forth. Change detection in these regions are mainly carried out via satellite images. Hence, an extensive knowledge on satellite image processing is necessary for historical map classification applications. An exhaustive analysis on the merits and demerits of many satellite image processing methods are discussed in this paper. Though several computational methods are available, different methods perform differently for the various satellite image processing applications. Wrong selection of methods will lead to inferior results for a specific application. This work highlights the methods and the suitable satellite imaging methods associated with these applications. Several comparative analyses are also performed in this work to show the suitability of several methods. This work will help support the selection of innovative solutions for the different problems associated with satellite image processing applications.

scholarly journals Photogrammetry and image processing techniques for beach monitoring

2020 ◽  
pp. 175
Author(s):  
Elena Sánchez-García ◽  
Ángel Balaguer-Beser ◽  
Josep Eliseu Pardo-Pascual
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Morphological Changes ◽  
Satellite Image ◽  
Public Information ◽  
Quality Data ◽  
Satellite Image Processing ◽  
Processing Techniques ◽  
Incident Waves ◽  
Derived Data

<p>The land-water boundary varies according to the sea level and the shape of a beach profile that is continuously modelled by incident waves. Attempting to model the response of a landscape as geomorphologically volatile as beaches requires multiple precise measurements to recognize responses to the actions of various geomorphic agents. It is therefore essential to have monitoring systems capable of systematically recording the shoreline accurately and effectively. New methods and tools are required to efficiently capture, characterize, and analyze information – and so obtain geomorphologically significant indicators. This is the aim of the doctoral thesis, focusing on the development of tools and procedures for coastal monitoring using satellite images and terrestrial photographs. The work brings satellite image processing and photogrammetric solutions to scientists, engineers, and coastal managers by providing results that demonstrate the usefulness of these viable and lowcost techniques. Existing and freely accessible public information (satellite images, video-derived data, or crowdsourced photographs) can be converted into high quality data for monitoring morphological changes on beaches and thus help achieve a sustainable management of coastal resources.</p>

Satellite Image Processing System (SIPS) - Design & Development

2005 ◽  
Author(s):  
T.C. Sarma ◽  
B. Lakshmi ◽  
D.S. Jain ◽  
B.J. Reddy ◽  
K.M.M. Rao ◽  
...  
Keyword(s):  
Image Processing ◽  
Satellite Image ◽  
Processing System ◽  
Design Development ◽  
Image Processing System ◽  
Satellite Image Processing

Analysis of the Computer Processing System of Remote Sensing Satellite Images

2013 ◽  
Vol 756-759 ◽  
pp. 3987-3991
Author(s):  
Chu Yan Li ◽  
Xian Wei Shi ◽  
Xiao Jing Li ◽  
Hai Yu Li ◽  
Lin Deng
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Satellite Images ◽  
Low Cost ◽  
Satellite Image ◽  
Processing System ◽  
Computer Processing ◽  
Image Processing System ◽  
Computer Image Processing ◽  
Remote Sensing Satellite

Remote sensing satellite images can intuitively reflect the information of the Earth's surface. The computer image processing system is of the advantages of high-precision and low-cost. It has a strong application value to study the computer processing system of remote sensing satellite image. The paper first discussed the design principles of the computer processing system and the implementation of its workflow, and then the application of the image processing system is briefly analyzed.

scholarly journals Supervised Classification of Satellite Image Processing using Neural Networks

2020 ◽  
Vol 9 (7) ◽  
pp. 26-31
Keyword(s):  
Neural Networks ◽  
Image Processing ◽  
Supervised Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Remote Sensing Technique ◽  
Erdas Imagine ◽  
Satellite Image Processing ◽  
Landsat Satellite

Now a day’s satellite image processing plays a major role. By using remote sensing technique, we can classify the satellite images like LISS (Linear image self-scanner), LANDSAT satellite image by using ERDAS imagine software. By using ERDAS imagine software, the classification of an satellite images will take more time. Rather than ERDAS imagine software we can use NEURAL NETWORKS in MATLAB software for classifying the satellite images by using the corresponding code with respect to the image by simply changing the file name. This paper includes the method like supervised and classification by using ERDAS imagine software and MATLAB code. The aim of this projects is to realize the image classification using NEURAL NETWORKS.

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).

scholarly journals Applying various satellite image processing methods on Aster and Landsat ETM+ data to identify and separate the alteration zones around gold mine of Akhtarchi, Khomein, Iran

2020 ◽  
Vol 33 (02) ◽  
pp. 490-510
Author(s):  
Alireza Payamani ◽  
Behnam Babaei ◽  
Saeed Dehghan ◽  
Houshang Asadi Harouni
Keyword(s):  
Image Processing ◽  
Satellite Images ◽  
Satellite Image ◽  
Gold Mine ◽  
Band Ratio ◽  
Processing Methods ◽  
Spectral Angle Mapper ◽  
Alteration Zones ◽  
Satellite Image Processing ◽  
Spectral Angle

The study area is located 100 km southeast of Arak and in two structural zones of Central Iran in the north and Sanandaj-Sirjan in the southern part. Regarding its geological structures, the area has become the source of important mines including the Akhtarchi gold mine, Aliabad iron mine, Ochestan feldspar mine, and Dali gold and copper mines. Therefore, promising areas for exploration activities are identified using the analysis of satellite images of ASTER and Landsat ETM + in the region to identify alteration areas. For this purpose, the necessary corrections were applied to the satellite images. Then, to identify the alteration parts related to the gold deposits, different satellite image processing methods of ETM + and ASTER were used.  These methods include making a false-color composite, band ratio, Selective Principal Components Analysis (SPCA), Spectral Angle Mapper (SAM) method, Spectral Information Divergence Classification (SID), Endmember Collection Dialog Components (ECDC), and innovative methods such as Principal Component Analysis (PCA) and Spectral Angle Mapper, as well as unsupervised classification methods. In the end, the major alterations in the region were observed. In the obtained images, the prophylitic zone and the phyllic and argillic zones in the region were observed. To introduce the optimal method, the results of the various methods mentioned were compared with each other and with the current situation of the mines. The alteration zones were identified through band ratio and SAM methods and the combined methods with more power. Finally, SAM, 2:1 ratio, and the combined methods were identified as successful methods for more accurate separation of the alteration zones.

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