An Improved RF-PNN Based Annotation Model With Texture and Edge Based Feature Descriptors

2021 ◽  
Author(s):  
Joshua Bapu J ◽  
Jemi Florinabel D
Keyword(s):  
Neural Network ◽  
Land Use ◽  
Satellite Images ◽  
Earth Surface ◽  
Informative Feature ◽  
Feature Descriptors ◽  
The Earth ◽  
Benchmark Datasets ◽  
Comparative Results ◽  
Edge Based

Abstract High informative feature descriptors always improves the classification process. In order to classify the earth surface, it is essential to annotate satellite images using itshigh informative feature descriptors. In this proposed work, an annotation framework has been implemented to improve the image discrimination by extracting texture and edge based feature vectors. So the combination of these features subsequently fed into the Random Forest based Probability Neural Network (RF-PNN) classifier to make an annotation model. The experimental analysis with comparisons shows that the proposed annotation model well performed with earlier works and comparative results of benchmark datasets of AID dataset, UC-Merced Land-Use dataset and WHU-RS19 datasets have been documented with analysis.

scholarly journals Assessment of the open-mining prevalence on the territory of Moscow region

2020 ◽  
Vol 28 (4) ◽  
pp. 349-360
Author(s):  
Ksenia O. Naumova ◽  
Elena V. Stanis
Keyword(s):  
Remote Sensing ◽  
Physicochemical Properties ◽  
Satellite Images ◽  
Mineral Resources ◽  
Moscow Region ◽  
Topographic Maps ◽  
Earth Surface ◽  
Construction Sites ◽  
Land Disturbance ◽  
The Earth

Lands disturbed by open cuts and associated dumps often become unsuitable for further use as construction sites. Disturbed lands are technogenically altered soils with new changed physicomechanical and physicochemical properties. The paper examines the results of researching the disturbed lands of common mineral resources open cuts in Moscow region, provides a description of the causes and types of land disturbance, as well as examples of images of disturbed lands on satellite images. For this purposes, thematic and topographic maps and remote sensing materials - satellite images of the territory of the Moscow region were used. The problems of impact of common mineral resources extraction on the natural complexes in the region, the scale of technogenic transformation of the earth surface as a result of open cuts mining as well as the geoecological problems arising in this case are considered. Numerical geoecological assessment of sand quarries in Moscow region is also given.

scholarly journals HISTORICAL URBAN FORM: A CASE STUDY OF MELAKA

2018 ◽  
Vol 16 ◽  
Author(s):  
Illyani Ibrahim ◽  
Alias Abdullah ◽  
Mansor Ibrahim ◽  
Fatin Farhana Murtaza
Keyword(s):  
Land Use ◽  
Content Analysis ◽  
Urban Form ◽  
Open Space ◽  
Earth Surface ◽  
Malay Peninsula ◽  
Form Analysis ◽  
The Earth

Understanding the urban form is crucial in determining the structure of a city in terms of physical and nonphysical aspects. The physical aspects include built-up areas that can be seen on the earth surface, and the nonphysical aspects include the shape, size, density, and configuration of settlements. The objectives of this study are to (i) analyse the elements of historical urban form that are suitable for the site and (ii) to study on the elements of urban form in Melaka. Content analysis was adopted to analyse the literature of urban form and Melaka. Results show that the following four elements of urban form are suitable to be used for historical urban form analysis: (i) streets, (ii) land use, (iii) buildings, and (iv) open space. The findings also indicate that the selected urban form has successfully delineated in the historical of Melaka as the selected urban elements can be specifically scrutinized with the content analysis. Further study will focus on the historical urban form within the Malay Peninsula.

scholarly journals HISTORICAL URBAN FORM: A CASE STUDY OF MELAKA

2018 ◽  
Vol 16 (6) ◽  
Author(s):  
Illyani Ibrahim ◽  
Alias Abdullah ◽  
Mansor Ibrahim ◽  
Fatin Farhana Murtaza
Keyword(s):  
Land Use ◽  
Content Analysis ◽  
Urban Form ◽  
Open Space ◽  
Earth Surface ◽  
Malay Peninsula ◽  
Form Analysis ◽  
The Earth

Understanding the urban form is crucial in determining the structure of a city in terms of physical and nonphysical aspects. The physical aspects include built-up areas that can be seen on the earth surface, and the nonphysical aspects include the shape, size, density, and configuration of settlements. The objectives of this study are to (i) analyse the elements of historical urban form that are suitable for the site and (ii) to study on the elements of urban form in Melaka. Content analysis was adopted to analyse the literature of urban form and Melaka. Results show that the following four elements of urban form are suitable to be used for historical urban form analysis: (i) streets, (ii) land use, (iii) buildings, and (iv) open space. The findings also indicate that the selected urban form has successfully delineated in the historical of Melaka as the selected urban elements can be specifically scrutinized with the content analysis. Further study will focus on the historical urban form within the Malay Peninsula.

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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