Local-Global Active Learning Based on a Graph Convolutional Network for Semi-Supervised Classification of Hyperspectral Imagery

• Published in: IEEE geoscience and remote sensing letters Vol. 20; pp. 1 - 5
• Main Authors: Ye, Zhen, Sun, Tao, Shi, Shihao, Bai, Lin, Fowler, James E.
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active learning (AL); Artificial neural networks; Classification; Convolution; Deep learning; Feature extraction; Fuses; hyperspectral image classification; Hyperspectral imaging; Image classification; Labeling; Machine learning; semi-supervised classification; Source code; Task analysis; Training
• ISSN: 1545-598X; 1558-0571
• DOI: 10.1109/LGRS.2023.3244758

Deep learning is being increasingly employed for hyperspectral classification, although such use is often predicated on the availability of a sufficiently large set of labeled samples for training. To improve classification performance under a limited training-set size, a semi-supervised network with end-to-end local-global active learning (AL) based on graph convolutional networks (GCNs) is proposed. The proposed AL extracts both global as well as local graph-based features to gauge the discriminative information in unlabeled samples, while semi-supervised classification expands the training set of a fully supervised classifier by attaching pseudo-labels to high-confidence unlabeled samples. Experimental results demonstrate that the proposed network outperforms not only other approaches to semi-supervised classification but also several existing fully supervised methods. The source code of this method can be found at https://github.com/XtaoS/semi-LG-AGCN .