TY - GEN
T1 - HSRRS Classification Method Based on Deep Transfer Learning and Multi-Feature Fusion
AU - Wang, Ziteng
AU - Li, Zhaojie
AU - Wang, Yu
AU - Li, Wenmei
AU - Yang, Jie
AU - Ohtsuki, Tomoaki
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Convolutional neural network (CNN) is one of the most important tools to accomplish high-spatial-resolution remote sensing (HSRRS) image classification tasks with their unique feature extraction and feature expression capabilities. However, the CNN-based classification method is very limited due to the acquisition of HSRRS images is difficult and the sample size is limited. In addition, the extraction of features by a single model is very limited, which limits the further improvement of classification performance. To solve the above problems, we propose ResNet50-InceptionV3 based on deep transfer learning and multi-feature fusion (TLMFFRI) model to apply for high-spatial-resolution remote sensing image classification. First, both ResNet50 and InceptionV3 are trained on the ImageNet dataset. Then, transfer the trained convolutional layers weights to the TLMFFRI model to fuse the features and realize the HSRRS image classification. Finally, we evaluate the method on the HSRRS dataset. Compared with ResNet50 based on transfer learning (TL-ResNet50) and InceptionV3 based on transfer learning (TL-InceptionV3), the proposed method achieved better classification performance.
AB - Convolutional neural network (CNN) is one of the most important tools to accomplish high-spatial-resolution remote sensing (HSRRS) image classification tasks with their unique feature extraction and feature expression capabilities. However, the CNN-based classification method is very limited due to the acquisition of HSRRS images is difficult and the sample size is limited. In addition, the extraction of features by a single model is very limited, which limits the further improvement of classification performance. To solve the above problems, we propose ResNet50-InceptionV3 based on deep transfer learning and multi-feature fusion (TLMFFRI) model to apply for high-spatial-resolution remote sensing image classification. First, both ResNet50 and InceptionV3 are trained on the ImageNet dataset. Then, transfer the trained convolutional layers weights to the TLMFFRI model to fuse the features and realize the HSRRS image classification. Finally, we evaluate the method on the HSRRS dataset. Compared with ResNet50 based on transfer learning (TL-ResNet50) and InceptionV3 based on transfer learning (TL-InceptionV3), the proposed method achieved better classification performance.
KW - Convolutional neural network (CNN)
KW - high-spatial-resolution remote sensing (HSRRS)
KW - multi-feature fusion
KW - transfer learning
UR - http://www.scopus.com/inward/record.url?scp=85123013946&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85123013946&partnerID=8YFLogxK
U2 - 10.1109/VTC2021-Fall52928.2021.9625348
DO - 10.1109/VTC2021-Fall52928.2021.9625348
M3 - Conference contribution
AN - SCOPUS:85123013946
T3 - IEEE Vehicular Technology Conference
BT - 2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 94th IEEE Vehicular Technology Conference, VTC 2021-Fall
Y2 - 27 September 2021 through 30 September 2021
ER -