An Active Deep Learning Approach for Minimally Supervised PolSAR Image Classification

• Published in: IEEE transactions on geoscience and remote sensing Vol. 57; no. 11; pp. 9378 - 9395
• Main Authors: Bi, Haixia, Xu, Feng, Wei, Zhiqiang, Xue, Yong, Xu, Zongben
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.11.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Active learning; Annotations; Artificial neural networks; Bibliographic information; Classification; convolutional neural network (CNN); data augmentation; Deep learning; Fields (mathematics); fine-tuning; Image classification; Learning systems; Machine learning; Markov random field (MRF); Neural networks; Pixels; polarimetric synthetic aperture radar (PolSAR) image classification; Radar imaging; Radar polarimetry; Remote sensing; SAR (radar); Smoothness; Synthetic aperture radar; Task analysis; Training

Recently, deep neural networks have received intense interests in polarimetric synthetic aperture radar (PolSAR) image classification. However, its success is subject to the availability of large amounts of annotated data which require great efforts of experienced human annotators. Aiming at improvi...