@inproceedings{d52cd952e6ed49cd98543a486f0e332f,
title = "Densely connected convolutional neural network based polarimetric sar image classification",
abstract = "With the development of representation learning, deep learning based methods have become the state-of-the-art method in some related fields of pattern recognition. This phenomenon brings new challenges and opportunities to polarimetric SAR image interpretation. In this paper, we propose a novel classification method for polarimetric SAR image based on a fresh technique in deep learning: DenseNet. A 20-layers (with 3 dense block and 2 transition layers) DenseNet is built to implement polarimetric SAR image classification. The proposed method effectively prevents gradient vanish and overfitting by feature reuse while automatically extracting high-level features and performing pixel-wise multi-class classification. Last but not least, the proposed method achieves the stateof- the-art experimental result on PolSAR Flevoland 15-class benchmark dataset.",
keywords = "Convolutional neural networks, Deep learning, DenseNet, Polarimetric SAR image classification",
author = "Hongwei Dong and Lamei Zhang and Bin Zou",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 39th IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 ; Conference date: 28-07-2019 Through 02-08-2019",
year = "2019",
doi = "10.1109/IGARSS.2019.8900292",
language = "英语",
series = "International Geoscience and Remote Sensing Symposium (IGARSS) ",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "3764--3767",
booktitle = "2019 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2019 - Proceedings",
address = "美国",
}