HSRRS Classification Method Based on Deep Transfer Learning and Multi-Feature Fusion

Ziteng Wang, Zhaojie Li, Yu Wang, Wenmei Li, Jie Yang, Tomoaki Ohtsuki

研究成果: Conference contribution


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.

ホスト出版物のタイトル2021 IEEE 94th Vehicular Technology Conference, VTC 2021-Fall - Proceedings
出版社Institute of Electrical and Electronics Engineers Inc.
出版ステータスPublished - 2021
イベント94th IEEE Vehicular Technology Conference, VTC 2021-Fall - Virtual, Online, United States
継続期間: 2021 9月 272021 9月 30


名前IEEE Vehicular Technology Conference


Conference94th IEEE Vehicular Technology Conference, VTC 2021-Fall
国/地域United States
CityVirtual, Online

ASJC Scopus subject areas

  • コンピュータ サイエンスの応用
  • 電子工学および電気工学
  • 応用数学


「HSRRS Classification Method Based on Deep Transfer Learning and Multi-Feature Fusion」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。