Deepcloud - A Fully Convolutionnal Neural Network for Cloud and Shadow Masking in Optical Satellite Images

2018 ◽  
Author(s):  
Matthieu Molinier ◽  
Niko Reunanen ◽  
Arttu Lamsa ◽  
Heikki Astola ◽  
Tomi Raty
Keyword(s):  
Neural Network ◽  
Satellite Images ◽  
Optical Satellite Images

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

A Novel Change Detection Method for Unregistered Optical Satellite Images

2012 ◽  
Vol E95.B (5) ◽  
pp. 1890-1893
Author(s):  
Wang LUO ◽  
Hongliang LI ◽  
Guanghui LIU ◽  
Guan GUI
Keyword(s):  
Change Detection ◽  
Satellite Images ◽  
Detection Method ◽  
Optical Satellite Images

scholarly journals CNN Algorithm for Roof Detection and Material Classification in Satellite Images

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1592
Author(s):  
Jonguk Kim ◽  
Hyansu Bae ◽  
Hyunwoo Kang ◽  
Suk Gyu Lee
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Satellite Imagery ◽  
Satellite Images ◽  
External Shocks ◽  
Satellite Imaging ◽  
Material Classification ◽  
Model Algorithm ◽  
Great Damage

This paper suggests an algorithm for extracting the location of a building from satellite imagery and using that information to modify the roof content. The materials are determined by measuring the conditions where the building is located and detecting the position of a building in broad satellite images. Depending on the incomplete roof or material, there is a greater possibility of great damage caused by disaster situations or external shocks. To address these problems, we propose an algorithm to detect roofs and classify materials in satellite images. Satellite imaging locates areas where buildings are likely to exist based on roads. Using images of the detected buildings, we classify the material of the roof using a proposed convolutional neural network (CNN) model algorithm consisting of 43 layers. In this paper, we propose a CNN structure to detect areas with buildings in large images and classify roof materials in the detected areas.

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