Pyramidal Dilation Attention Convolutional Network With Active and Self-Paced Learning for Hyperspectral Image Classification

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 16; pp. 1503 - 1518
• Main Authors: Hou, Wenhui, Chen, Na, Peng, Jiangtao, Sun, Weiwei, Du, Qian
• 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; Convolutional neural networks; Deep learning; deep learning (DL); Dilation; Feature extraction; Field investigations; hyperspectral image (HSI) classification; Hyperspectral imaging; Image classification; Labeling; Labels; Machine learning; Methods; Neural networks; self-paced learning (SPL); Support vector machines; Training
• ISSN: 1939-1404; 2151-1535
• DOI: 10.1109/JSTARS.2023.3237566

In recent years, deep neural networks have been widely used for hyperspectral image (HSI) classification and have shown excellent performance using numerous labeled samples. The acquisition of HSI labels is usually based on the field investigation, which is expensive and time consuming. Hence, the available labels are usually limited, which affects the efficiency of deep HSI classification methods. To improve the classification performance while reducing the labeling cost, this article proposes a semisupervised deep learning (DL) method for HSI classification, named pyramidal dilation attention convolutional network with active and self-paced learning (PDAC-ASPL), which integrates active learning (AL), self-paced learning (SPL), and DL into a unified framework. First, a densely connected pyramidal dilation attention convolutional network is trained with a limited number of labeled samples. Then, the most informative samples from the unlabeled set are selected by AL and queried real labels, and the highest confidence samples with corresponding pseudo labels are extracted by SPL. Finally, the samples from AL and SPL are added to the training set to retrain the network. Compared with some DL- and AL-based HSI classification methods, our PDAC-ASPL achieves better performance on four HSI datasets.