TY - GEN
T1 - Urban Area Change Detection with Combining CNN and RNN from Sentinel-2 Multispectral Remote Sensing Data
AU - Khusni, Uus
AU - Dewangkoro, Herdito Ibnu
AU - Arymurthy, Aniati Murni
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/9/15
Y1 - 2020/9/15
N2 - Change detection is one of the hot issues related to world observation and has been extensively studied in recent decades. The application of remote sensing technology provides inputs to systems for urban change detection primarily focus on the urban data user environment. Urban change detection refers to the general problem of monitoring the urban system and discerning changes that are occurring within that system that use to urban planners, managers, and researchers. Current methods based on a simple mechanism for independently encoding bi-temporal images to get their representation vectors. In fact, these methods do not make full use of the rich information between bi-temporal images. We propose to combine deep learning methods such as Convolutional Neural Network (U-Net) for feature extraction and Recurrent Neural Network (BiLSTM) temporal modeling. Our developed model while the validation phase gets 97.418% overall accuracy on the Onera Satellite Change Detection (OSCD) Sentinel-2 bi-temporal dataset.
AB - Change detection is one of the hot issues related to world observation and has been extensively studied in recent decades. The application of remote sensing technology provides inputs to systems for urban change detection primarily focus on the urban data user environment. Urban change detection refers to the general problem of monitoring the urban system and discerning changes that are occurring within that system that use to urban planners, managers, and researchers. Current methods based on a simple mechanism for independently encoding bi-temporal images to get their representation vectors. In fact, these methods do not make full use of the rich information between bi-temporal images. We propose to combine deep learning methods such as Convolutional Neural Network (U-Net) for feature extraction and Recurrent Neural Network (BiLSTM) temporal modeling. Our developed model while the validation phase gets 97.418% overall accuracy on the Onera Satellite Change Detection (OSCD) Sentinel-2 bi-temporal dataset.
KW - BiLSTM
KW - change detection
KW - convolutional neural network
KW - LSTM
KW - recurrent neural network
UR - http://www.scopus.com/inward/record.url?scp=85098977621&partnerID=8YFLogxK
U2 - 10.1109/IC2IE50715.2020.9274617
DO - 10.1109/IC2IE50715.2020.9274617
M3 - Conference contribution
AN - SCOPUS:85098977621
T3 - 2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
SP - 171
EP - 175
BT - 2020 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
A2 - Hermawan, Indra
A2 - Rasyidin, Muhammad Yusuf Bagus
A2 - Huzaifa, Malisa
A2 - Ermis Ismail, Iklima
A2 - Muharram, Asep Taufik
A2 - Mardiyono, Anggi
A2 - Marcheeta, Noorlela
A2 - Kurniawati, Dewi
A2 - Yuly, Ade Rahma
A2 - Suhanda, Ariawan Andi
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Computer and Informatics Engineering, IC2IE 2020
Y2 - 15 September 2020 through 16 September 2020
ER -