DNN-Based PolSAR Image Classification on Noisy Labels

• Published in: IEEE journal of selected topics in applied earth observations and remote sensing Vol. 15; pp. 3697 - 3713
• Main Authors: Ni, Jun, Xiang, Deliang, Lin, Zhiyuan, Lopez-Martinez, Carlos, Hu, Wei, Zhang, Fan
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Classification; Classifiers; Deep learning; Deep neural network (DNN); Image classification; Labeling; Labels; Neural networks; Noise measurement; noisy label; polarimetric synthetic aperture radar (SAR); Probability; Probability theory; Radar polarimetry; Remote sensing; Samples; SAR (radar); Statistical methods; Statistics; Synthetic aperture radar; Training

Deep neural networks (DNNs) appear to be a solution for the classification of polarimetric synthetic aperture radar (PolSAR) data in that they outperform classical supervised classifiers under the condition of sufficient training samples. The design of a classifier is challenging because DNNs can ea...