TY - GEN
T1 - LIGHTWEIGHT FINE-GRAINED RECOGNITION METHOD BASED ON MULTILEVEL FEATURE WEIGHTED FUSION
AU - Pan, Yu
AU - Tang, Linbo
AU - Zhao, Baojun
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Fine-grained recognition in remote sensing images has played a critical role in military and civil fields. Recently, with the rapid growth of convolutional neural networks (CNNs), many fine-grained recognition methods have been proposed. However, due to the large amount of parameters and computational complexity, it is difficult to apply these methods in practical applications. To this end, we propose a novel lightweight fine-grained recognition method based on multilevel feature weighted fusion. First, we design a lightweight CNN (LCNN) framework. Second, we propose a multilevel feature weighted fusion method to improve the recognition accuracy. Third, we adopt a feature channel based loss function to train the proposed model end-to-end. Experiments are conducted on the challenging remote sensing dataset MTARSI to evaluate our proposed method. The results show that the proposed method can achieve state-of-the-art performance.
AB - Fine-grained recognition in remote sensing images has played a critical role in military and civil fields. Recently, with the rapid growth of convolutional neural networks (CNNs), many fine-grained recognition methods have been proposed. However, due to the large amount of parameters and computational complexity, it is difficult to apply these methods in practical applications. To this end, we propose a novel lightweight fine-grained recognition method based on multilevel feature weighted fusion. First, we design a lightweight CNN (LCNN) framework. Second, we propose a multilevel feature weighted fusion method to improve the recognition accuracy. Third, we adopt a feature channel based loss function to train the proposed model end-to-end. Experiments are conducted on the challenging remote sensing dataset MTARSI to evaluate our proposed method. The results show that the proposed method can achieve state-of-the-art performance.
UR - http://www.scopus.com/inward/record.url?scp=85126014182&partnerID=8YFLogxK
U2 - 10.1109/IGARSS47720.2021.9553338
DO - 10.1109/IGARSS47720.2021.9553338
M3 - Conference contribution
AN - SCOPUS:85126014182
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4767
EP - 4770
BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Y2 - 12 July 2021 through 16 July 2021
ER -