TY - GEN
T1 - Recognition For Underground Voids in C-SCANS Based On LSTM Using Ground Penetrating Radar
AU - Bai, Xu
AU - Zhang, Yang
AU - Feng, Pengfei
AU - Chen, Guanyi
AU - Liu, Jinglong
AU - Wen, Zhitao
AU - Tian, Haoxiang
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Ground Penetrating Radar, as a non-invasive detection instrument, is widely used for shallow underground environment exploring. However, as the interpretation for underground voids is still manually performed, the process is inefficient. Aiming at the challenges of automatic recognition for underground voids, we proposed a recognition algorithm based on Long Short-Term Memory(LSTM), which can recognize 3D underground voids automatically. First, we perform energy detection on the images to obtain the preprocessed and prescreened data. Next, EHD, HOG, and Log-Gabor filters are used to extract features. As the traditional methods can only be applied to 2D images, preprocessing for C-scans is employed. Finally, the aggregated features are fed into LSTM for classification. In the experiment, the method is evaluated on simulation data sets, and obtaining a 90% accuracy, which proved the effectiveness and efficiency of our method.
AB - Ground Penetrating Radar, as a non-invasive detection instrument, is widely used for shallow underground environment exploring. However, as the interpretation for underground voids is still manually performed, the process is inefficient. Aiming at the challenges of automatic recognition for underground voids, we proposed a recognition algorithm based on Long Short-Term Memory(LSTM), which can recognize 3D underground voids automatically. First, we perform energy detection on the images to obtain the preprocessed and prescreened data. Next, EHD, HOG, and Log-Gabor filters are used to extract features. As the traditional methods can only be applied to 2D images, preprocessing for C-scans is employed. Finally, the aggregated features are fed into LSTM for classification. In the experiment, the method is evaluated on simulation data sets, and obtaining a 90% accuracy, which proved the effectiveness and efficiency of our method.
KW - EHD
KW - GPR
KW - HOG
KW - LSTM
KW - Log-Gabor
KW - Recognition
UR - https://www.scopus.com/pages/publications/85140409100
U2 - 10.1109/IGARSS46834.2022.9883689
DO - 10.1109/IGARSS46834.2022.9883689
M3 - 会议稿件
AN - SCOPUS:85140409100
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3520
EP - 3523
BT - IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2022
Y2 - 17 July 2022 through 22 July 2022
ER -