TY - GEN
T1 - Attention based network for remote sensing scene classification
AU - Liu, Shaoteng
AU - Wang, Qi
AU - Li, Xuelong
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/31
Y1 - 2018/10/31
N2 - Scene classification of very high resolution remote sensing images is becoming more and more important because of its wide range of applications. However, previous works are mainly based on handcrafted features which do not have enough adaptability and expression ability. In this paper, inspired by the attention mechanism of human visual system, we propose a novel attention based network (AttNet) for scene classification. It can focus selectively on some key areas of images so that it can abandon redundant information. Essentially, AttNet gives a way to readjust the signal of supervision, and it is one of the first successful attempts on visual attention for remote sensing scene classification. Our method is evaluated on the UC Merced Land-Use Dataset, in comparison with some state-of-the-art methods. The experimental result shows that the proposed method makes a great improvement on both convergence speed and classification accuracy, and it also shows the effectiveness of visual attention for this task.
AB - Scene classification of very high resolution remote sensing images is becoming more and more important because of its wide range of applications. However, previous works are mainly based on handcrafted features which do not have enough adaptability and expression ability. In this paper, inspired by the attention mechanism of human visual system, we propose a novel attention based network (AttNet) for scene classification. It can focus selectively on some key areas of images so that it can abandon redundant information. Essentially, AttNet gives a way to readjust the signal of supervision, and it is one of the first successful attempts on visual attention for remote sensing scene classification. Our method is evaluated on the UC Merced Land-Use Dataset, in comparison with some state-of-the-art methods. The experimental result shows that the proposed method makes a great improvement on both convergence speed and classification accuracy, and it also shows the effectiveness of visual attention for this task.
KW - Convolutional neural networks
KW - Deep learning
KW - Long short-term memory
KW - Remote sensing
KW - Scene classification
KW - Visual attention
UR - http://www.scopus.com/inward/record.url?scp=85064157076&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2018.8519232
DO - 10.1109/IGARSS.2018.8519232
M3 - 会议稿件
AN - SCOPUS:85064157076
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 4740
EP - 4743
BT - 2018 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Y2 - 22 July 2018 through 27 July 2018
ER -
