Complex-Valued Convolutional Neural Network and Its Application in Polarimetric SAR Image Classification

By Zhimian Zhang, Haipeng Wang, Feng Xu, Yaqiu Jin. IEEE Transactions on Geoscience and Remote Sensing, 2017

604 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/e614320cec19b1e037c2b337b594a0448373de2e

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. Richard Bamler
2. Xiaoxiang Zhu
3. Feng Xu
4. Sudipan Saha
5. Lichao Mou
6. Jianwei Ma
7. Siwei Yu

In Ocean Engineering

1. Siwei Yu
2. Muhammad Shahzad
3. Qian Song
4. Xiao Xiang Zhu
5. Feng Xu
6. Sudipan Saha
7. Lichao Mou
8. Jianwei Ma

Citations in the corpus

• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “Although CV-CNNs are capable of learning phase information and show great potential in processing CV-SAR images, only a few such attempts have been made [76].”
• Unsupervised Single-Scene Semantic Segmentation for Earth Observation (2022, methodology): “This dataset [65] is acquired by ESAR L-band sensor.”
• Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “To consider both the amplitude and phase information of complex SAR imagery, a complex-valued CNN for SAR image classification was proposed to process complex-valued inputs (Zhang, Wang, et al., 2017).”