Band selection of satellite images using Histogram distribution and Mineral Mapping using Artificial Neural Network

2019 ◽  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Images ◽  
Band Selection ◽  
Mineral Mapping ◽  
Artificial Neural ◽  
Selection Of

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

scholarly journals Automatic Extraction of the Non-Urban Roads from Satellite Images using Artificial Neural Network

2020 ◽  
Vol 3 (1) ◽  
pp. 491-500
Author(s):  
Matin Ghaziani ◽  
Erhan İlhan Konukseven ◽  
Ahmet Buğra Koku
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Images ◽  
Aerial Image ◽  
Source Image ◽  
Road Extraction ◽  
Segmentation Method ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Neural Network Input

Road detection from the satellite images can be considered as a classification process in which pixels are divided into the road and non-road classes. In this research, an automatic road extraction using an artificial neural network (ANN) based on automatic information extraction from satellite images and self-adjusting of the hidden layer proposed. Parameters of non-urban road networks from satellite images using a histogram-based binary image segmentation technique are also presented. The segmentation method is implemented by determining a global threshold, which is obtained from a statistical analysis of a number of sample satellite images and their ground truths. The thresholding method is based on two major facts: first, the points corresponding to non-asphalt roads are brighter than other areas in non-urban images. Second, it is observed that in an aerial image, the area covered by roads is only a small fraction of total pixels. It is also observed that pixels corresponding to roads are generally populated at the very bright end of the image greyscale histogram. In this method, at first, the possible road pixels are selected by the proposed segmentation method. Then different parameters, including color, gradient, and entropy, are computed for each pixel from the source image. Finally, these features are used for the artificial neural network input. The results show that the accuracy of the proposed road extraction method is around 80%.

scholarly journals Artificial neural network model for precipitation forecast over Western Himalaya using satellite images

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 83-90
Author(s):  
PIYUSH JOSHI ◽  
M.S. SHEKHAR ◽  
ASHAVANI KUMAR ◽  
J.K. QUAMARA
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Images ◽  
India Meteorological Department ◽  
Western Himalaya ◽  
Probability Of Detection ◽  
Precipitation Forecast ◽  
Ann Model ◽  
Skill Scores ◽  
Artificial Neural

Kalpana satellite images in real time available by India meteorological department (IMD), contain relevant inputs about the cloud in infra-red (IR), water vapor (WV), and visible (VIS) bands. In the present study an attempt has been made to forecast precipitation at six stations in western Himalaya by using extracted grey scale values of IR and WV images. The extracted pixel values at a location are trained for the corresponding precipitation at that location. The precipitation state at 0300 UTC is considered to train the model for precipitation forecast with 24 hour lead time. The satellite images acquired in IR (10.5 - 12.5 µm) and WV (5.7 - 7.1 µm) bands have been used for developing Artificial Neural Network (ANN) model for qualitative as well as quantitative precipitation forecast. The model results are validated with ground observations and skill scores are computed to check the potential of the model for operational purpose. The probability of detection at the six stations varies from 0.78 for Gulmarg in Pir-Panjal range to 0.95 for Dras in Greater Himalayan range. Overall performance for qualitative forecast is in the range from 61% to 84%. Root mean square error for different locations under study is in the range 5.81 to 8.7.

scholarly journals Automatic Normalization of Multitemporal Satellite Images using Artificial Neural Network and Unchanged Pixels

2014 ◽  
Vol 2 (1) ◽  
pp. 17-39
Author(s):  
Vahid Sadeghi ◽  
Hamid Ebadi ◽  
Farshid Farnood Ahmadi ◽  
◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Satellite Images ◽  
Artificial Neural

A review of the artificial neural network surrogate modeling in aerodynamic design

2019 ◽  
Vol 233 (16) ◽  
pp. 5863-5872 ◽  
Author(s):  
Gang Sun ◽  
Shuyue Wang
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Design Space ◽  
Optimal Solution ◽  
Surrogate Modeling ◽  
Aerodynamic Design ◽  
Hidden Information ◽  
Data Manipulation ◽  
Artificial Neural ◽  
Selection Of

Artificial neural network surrogate modeling with its economic computational consumption and accurate generalization capabilities offers a feasible approach to aerodynamic design in the field of rapid investigation of design space and optimal solution searching. This paper reviews the basic principle of artificial neural network surrogate modeling in terms of data treatment and configuration setup. A discussion of artificial neural network surrogate modeling is held on different objectives in aerodynamic design applications, various patterns of realization via cutting-edge data technique in numerous optimizations, selection of network topology and types, and other measures for improving modeling. Then, new frontiers of modern artificial neural network surrogate modeling are reviewed with regard to exploiting the hidden information for bringing new perspectives to optimization by exploring new data form and patterns, e.g. quick provision of candidates of better aerodynamic performance via accumulated database instead of random seeding, and envisions of more physical understanding being injected to the data manipulation.

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