Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks

By Yu Zhou, Haipeng Wang, Feng Xu, Yaqiu Jin. IEEE Geoscience and Remote Sensing Letters, 2016

450 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/1d85fbeccfcf50ad7aec9f615ca886c230ae5d7e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Xiaoxiang Zhu
2. Richard Bamler
3. Lichao Mou
4. Feng Xu
5. Zhimian Zhang
6. Feng Xu
7. Haipeng Wang
8. Yaqiu Jin

In Environmental Engineering

1. Feng Xu
2. Richard Bamler
3. Friedrich Fraundorfer
4. Haipeng Wang
5. Feng Xu
6. Yaqiu Jin
7. Devis Tuia
8. Lichao Mou

Citations in the corpus

• Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification (2017, methodology): “[9] applied convolutional network to POLSAR image classification and achieved a 92% accuracy on the Flevoland benchmark data.”
• Can deep learning beat numerical weather prediction? (2021, methodology): “Zhou et al. [83] and Denby et al. [84] used a CNN for classification of weather satellite images, while Xu et al. [85] used a combination of GAN and LSTM for prediction of cloud images.”
• Deep learning in remote sensing: a review (2017, methodology): “[65] applied CNNs to PolSAR image classification; here, a covariance matrix is extracted as six real-channel data input.”
• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “In [77], Zhou et al. first extracted a 6-channel covariance matrix and then fed it to a trainable CNN for PolSAR image classification.”