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 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 Pemetaan Mangrove Kepulauan Lease, Provinsi Maluku Menggunakan Data Multi-Temporal Dan Multi-Sensor Citra Satelit Landsat

2018 ◽  
Vol 3 (1) ◽  
pp. 19
Author(s):  
Sam Wouthuyzen ◽  
Fasmi Ahmad
Keyword(s):  
Cloud Cover ◽  
Supervised Classification ◽  
Satellite Images ◽  
Satellite Image ◽  
Multi Temporal ◽  
The Mean ◽  
Landsat 5 Tm ◽  
Landsat Satellite ◽  
The Cost ◽  
Near Future

<strong>Mangrove Mapping of The Lease Islands, Maluku Province Using Multi-Temporal And Multi-Sensor Of Landsat Satellite Images.</strong> Mangrove mapping in the Lease Islands, Maluku Province has been done, but using only a single date satellite image. Therefore, it is difficult to know the dynamics of their changes.  The aim of this study is to map mangroves every 5 year (1985-2015) using multi-sensors (MSS, TM, ETM+ and OLI) of Landsat and field data. Supervised classification using maximum likelihood was used for classifying mangrove and other habitats, and counting their areas. Results showed that mangrove in the Saparua and Nusalaut Islands, consisted of 22 and 13 species, respectively, with the longest distribution along the cost line of Tuhaha Bay due to freshwater supplay from the surrounding river, while the rest are grown in the hardy reef flat substrates. The mean overall acurracies of the maps was good enough (74.7%), except for one Landsat-5 TM and Landat-8 OLI because of the influences of cloud cover or haze.  During 30 years, the areas of mangrove are relatively stable since they are protected by local wisdom called "Kewang". The highest bias of 11.4% that made the areas of mangrove increase or decrease was not due to the utilization or conversion of mangrove, but mainly due to the influences of cloud cover/haze and the geometric differences among Landsat sensors. In the near future, the OBIA method should be try, because it seems to be able to produce mangrove maps with better accuracy.

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>

Geoinformation carthography of the landscapes of the Mondy depression

2018 ◽  
Vol 934 (4) ◽  
pp. 23-30 ◽  
Author(s):  
E.A. Istomina ◽  
E.V. Ovchinnikova
Keyword(s):  
Large Scale ◽  
Satellite Images ◽  
Satellite Image ◽  
Neural Network Classification ◽  
The Neural Network ◽  
Digital Elevation ◽  
Elevation Model ◽  
Landsat Satellite ◽  
Landscape Map

A method of typological mapping of landscapes with the use of Landsat satellite images and the digital elevation model SRTM, as well as the method of factorial-dynamic classification of landscapes, was developed and a large-scale landscape map of the Mondy basin was created. At the first stage, the image was automatically classified using the neural network classification method, resulting in a picture divided into 11 classes. The resulting classified image was smoothed to remove the mosaic effect and translated into a vector map. For each unit obtained as a result of the classification of the satellite image, the following parameters were calculated by means of spatial analysis in the GIS

scholarly journals Satellite Image Classification for Environmental Analysis using Image Processing

2020 ◽  
Vol 9 (3) ◽  
pp. 1513-1517
Keyword(s):  
Image Processing ◽  
Image Classification ◽  
Environmental Analysis ◽  
Supervised Classification ◽  
Satellite Image ◽  
Loss Of Life ◽  
Traditional Technique ◽  
Satellite Image Classification ◽  
The Future

Conventional supervised classification of satellite pictures utilizes a solitary multi-band picture and incidental ground perceptions to build phantom marks of land spread classes. We contrasted this methodology with three choices that get marks from different pictures and timespans. signature speculation, in this unearthly marks, is gotten from various pictures inside one season, however maybe from various years. signature extension, in this phantom marks, is made with information from pictures obtained during various periods of that year; and mixes of development and speculation. Utilizing the information for India, we evaluated the nature of these various marks to characterize the pictures used to infer the mark, and for use in transient mark expansion, i.e., applying a mark acquired from the information of one or quite a long while to pictures from different years. While applying marks to the pictures they were gotten from, signature development improved exactness comparative with the customary strategy, and inconstancy in precision declined uniquely. Conversely, signature speculation didn't improve grouping. While applying marks to pictures of different years (worldly expansion), the traditional technique, utilizing a mark got from a solitary picture, brought about extremely low characterization precision. Mark's development additionally performed ineffectively yet multi-year signature speculation performed much better and this seems, by all accounts, to be a promising methodology in the transient augmentation of ghastly marks for satellite picture arrangements. This project summarizes the different audits on satellite picture characterization strategies and systems. The summary helps the analysts to choose suitable satellite picture characterization strategies or methods dependent on the requirements. Later on, the results acquired from the proposed technique will be an extraordinary measure for anticipating and examining the effect of floods. It will help salvage groups to address high caution regions first in this way, least or no loss of life will be accomplished. In the future, the technique can be adjusted to be utilized for coastline location, urbanization, deforestation, and seismic tremors.

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.

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.

Multitemporal distribution analysis of Dodonaea viscosa (L.) Jacq. by remote sensing in Durango, Mexico

2020 ◽  
Vol 13 (8) ◽  
Author(s):  
Marco, A. Márquez-Linares ◽  
Jonathan G. Escobar--Flores ◽  
Sarahi Sandoval- Espinosa ◽  
Gustavo Pérez-Verdín
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Methodology ◽  
Supervised Classification ◽  
Satellite Images ◽  
Ground Cover ◽  
Distribution Analysis ◽  
Dodonaea Viscosa ◽  
Artificial Neural ◽  
Landsat Satellite

Objective: to determine the distribution of D. viscosa in the vicinity of the Guadalupe Victoria Dam in Durango, Mexico, for the years 1990, 2010 and 2017.Design/Methodology/Approach: Landsat satellite images were processed in order to carry out supervised classifications using an artificial neural network. Images from the years 1990, 2010 and 2017 were used to estimate ground cover of D. viscosa, pastures, crops, shrubs, and oak forest. This data was used to calculate the expansion of D. viscosa in the study area.Results/Study Limitations/Implications: the supervised classification with the artificial neural network was optimal after 400 iterations, obtaining the best overall precision of 84.5 % for 2017. This contrasted with the year 1990, when overall accuracy was low at 45 % due to less training sites (fewer than 100) recorded for each of the land cover classes.Findings/Conclusions: in 1990, D. viscosa was found on only five hectares, while by 2017 it had increased to 147 hectares. If the disturbance caused by overgrazing continues, and based on the distribution of D. viscosa, it is likely that in a few years it will have the ability to invade half the study area, occupying agricultural, forested, and shrub areas

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