TY - GEN
T1 - Scene Classification of High Resolution Remote Sensing Images Via Self-Paced Deep Learning
AU - Yao, Xiwen
AU - Yang, Liuqing
AU - Cheng, Gong
AU - Han, Junwei
AU - Guo, Lei
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/7
Y1 - 2019/7
N2 - Scene classification of high resolution remote sensing (HRRS) images is a fundamental yet challenging problem for remote sensing image analysis. In this paper, we focus on tackling the problem of HRSS scene classification using a small pool of unlabeled images and only a few labeled images per category, namely, few-shot scene classification (FSSC), which is more challenging than common scene classification task. The key challenge arises from selecting trustworthy samples from the pool of unlabeled images that have high confidence. To address this challenge, a novel local manifold constrained self-paced deep learning method is proposed. Specifically, the model is learned by gradually selecting easy samples from the pool of unlabeled images, assigning them with pseudo-labels and further adopting them with labeled images as the new training set. In addition, a local manifold constraint is introduced to enforce that the pseudo-labels assigned by the initial model should be consistent with the local manifold of the labeled samples. In such way, the confidence of the selecting samples is increased and is beneficial to train more robust classifier. Experimental results on a publicly available large scale NWPU-RESISC45 data set demonstrated the effectiveness of our method in achieving competitive performance while significantly reducing manually labeled cost.
AB - Scene classification of high resolution remote sensing (HRRS) images is a fundamental yet challenging problem for remote sensing image analysis. In this paper, we focus on tackling the problem of HRSS scene classification using a small pool of unlabeled images and only a few labeled images per category, namely, few-shot scene classification (FSSC), which is more challenging than common scene classification task. The key challenge arises from selecting trustworthy samples from the pool of unlabeled images that have high confidence. To address this challenge, a novel local manifold constrained self-paced deep learning method is proposed. Specifically, the model is learned by gradually selecting easy samples from the pool of unlabeled images, assigning them with pseudo-labels and further adopting them with labeled images as the new training set. In addition, a local manifold constraint is introduced to enforce that the pseudo-labels assigned by the initial model should be consistent with the local manifold of the labeled samples. In such way, the confidence of the selecting samples is increased and is beneficial to train more robust classifier. Experimental results on a publicly available large scale NWPU-RESISC45 data set demonstrated the effectiveness of our method in achieving competitive performance while significantly reducing manually labeled cost.
KW - Few-shot scene classification
KW - remote sensing images
KW - self-paced learning
UR - http://www.scopus.com/inward/record.url?scp=85077720406&partnerID=8YFLogxK
U2 - 10.1109/IGARSS.2019.8898387
DO - 10.1109/IGARSS.2019.8898387
M3 - 会议稿件
AN - SCOPUS:85077720406
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 521
EP - 524
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 -