TY - GEN
T1 - Very High Resolution Image Scene Classification with Capsule Network
AU - Chaib, Souleyman
AU - Amin Larabi, Mohammed El
AU - Gu, Yanfeng
AU - Bakhti, Khadidja
AU - Karoui, Moussa Sofiane
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Convolutional Neural Network (CNN) has boosted the performance of Very High Resolution (VHR) remote sensing data classification. Moreover, the continuous development of CNN techniques for image scenes description has entered a new challenge. The deep neural network models require a huge number of training samples, which is the main limitation of processing remote sensing data. To overcome this issue, a new method, based on the Capsule neural network for VHR image scenes recognition is proposed in this work. Experiments on the public Aerial Image Dataset (AID) benchmark, containing different areal categories with sub-meter spatial resolution are conducted. The obtained results demonstrate the effectiveness of the proposed method, as compared with the classical CNN model.
AB - Convolutional Neural Network (CNN) has boosted the performance of Very High Resolution (VHR) remote sensing data classification. Moreover, the continuous development of CNN techniques for image scenes description has entered a new challenge. The deep neural network models require a huge number of training samples, which is the main limitation of processing remote sensing data. To overcome this issue, a new method, based on the Capsule neural network for VHR image scenes recognition is proposed in this work. Experiments on the public Aerial Image Dataset (AID) benchmark, containing different areal categories with sub-meter spatial resolution are conducted. The obtained results demonstrate the effectiveness of the proposed method, as compared with the classical CNN model.
KW - Capsule network
KW - Scene recognition
KW - convolutional neural network
KW - very high resolution images
UR - https://www.scopus.com/pages/publications/85077695702
U2 - 10.1109/IGARSS.2019.8898104
DO - 10.1109/IGARSS.2019.8898104
M3 - 会议稿件
AN - SCOPUS:85077695702
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 3049
EP - 3052
BT - 2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019
Y2 - 28 July 2019 through 2 August 2019
ER -