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Deep Learning-Based Change Detection Method for Environmental Change Monitoring Using Sentinel-2 Datasets
Content Provider | MDPI |
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Author | Ahangarha, Marjan Shah-Hosseini, Reza Saadatseresht, Mohammad |
Copyright Year | 2020 |
Description | Change detection (CD) is an essential tool for the accurate understanding of land surface changes using Earth observation data and is extremely important for detecting the interactions between social and natural occurrences in geoscience. Binary change detection aims to detect changes and no changing areas, since improving the quality of the binary CD map is an important issue in remote sensing images; in this paper, a supervised deep learning (DL)-based change detection method was proposed to generate an accurate change map. Due to the good performance and great potential of DL in the domain of pattern recognition and nonlinear problem modeling, DL is becoming popular to resolve the CD problem using multitemporal remote sensing imageries. The purpose of using DL algorithms and especially convolutional neural networks (CNN) is to monitor the environmental change into change and no change classes. The Onera Satellite Change Detection (OSCD) datasets were used to evaluate the proposed method. Experimental results on the real dataset showed the effectiveness of the proposed algorithm. The overall accuracy and the kappa coefficient of the change map using the proposed method is over 95% and close to one, respectively. |
Starting Page | 15 |
e-ISSN | 26734931 |
DOI | 10.3390/IECG2020-08544 |
Journal | Environmental Sciences Proceedings |
Issue Number | 1 |
Volume Number | 5 |
Language | English |
Publisher | MDPI |
Publisher Date | 2020-11-25 |
Access Restriction | Open |
Subject Keyword | Environmental Sciences Proceedings Remote Sensing Change Detection Sentinel Deep Learning U-net |
Content Type | Text |