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Manuscript Details - IJCIRAS1461

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Paper Id: IJCIRAS1461
Title: LAND USE/COVER CHANGE DETECTION ANALYSIS USING DEEP LEARNING
Published in: International Journal Of Creative and Innovative Research In All Studies
Publisher: IJCIRAS
ISSN: 2581-5334
Volume / Issue: Volume 2 Issue 7
Pages: 4
Published On: 12/26/2019 8:07:12 AM      (MM/dd/yyyy)
Main Author Details
Name: Su Wit Yi Aung
Institute: University of Technology (Yatanarpon Cyber City), Pyin Oo Lwin, Myanmar
Co - Author Details
Author Name Author Institute
Hnin Aye Thant University of Technology (Yatanarpon Cyber City), Pyin Oo Lwin, Myanmar
Abstract
Research Area: Artificial Intelligence
KeyWord: Deep Convolutional Neural Network, Google Earth Satellite Images, Multi-labeled land use/ cover change detection, Temporal differencing, Confusion Matrices
Abstract: In recent years, Deep Convolutional Neural Network (DCNN) has been used increasingly in image change detection works but there is a need to investigate its potential. This study aims to propose a technique for multi-labeled land use/ cover change detection using Google Earth Satellite images with deep convolutional neural network (DCNN). Temporal differencing method is used for calculation of land cover change area. Then the results of land cover classification are analyzed using confusion matrices. According to the tested results, 90.7% of building index, 82.9% road index, 87% of vegetation index and 90% of water index can be correctly classified using DCNN.
Citations
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IEEE
Su Wit Yi Aung, Hnin Aye Thant, "Land Use/Cover Change Detection Analysis Using Deep Learning", International Journal Of Creative and Innovative Research In All Studies, vol. 2, no. 7, pp. 41-44, 2019.
MLA Su Wit Yi Aung, Hnin Aye Thant "Land Use/Cover Change Detection Analysis Using Deep Learning." International Journal Of Creative and Innovative Research In All Studies, vol 2, no. 7, 2019, pp. 41-44.
APA Su Wit Yi Aung, Hnin Aye Thant (2019). Land Use/Cover Change Detection Analysis Using Deep Learning. International Journal Of Creative and Innovative Research In All Studies, 2(7), 41-44.
Land Use/Cover Change Detection Analysis Using Deep Learning
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