Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

By Zhongling Huang, Zongxu Pan, Bin Lei. Remote Sensing, 2017

386 citations recorded by the source corpus.

Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway.

Subfields: Aerospace Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/db14833dde228b911c7a1cbf025fdd78736eb520

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. Mohammad Rostami
2. Eric Eaton
3. Kyungnam Kim
4. Soheil Kolouri
5. Kun Fu
6. Xian Sun
7. Serban Stan

In Ocean Engineering

1. Eric Eaton
2. Mohammad Rostami
3. Kun Fu
4. Xian Sun

Citations in the corpus

• Learning Transferable Knowledge Through Embedding Spaces (2019, methodology): “Building upon prior works in the area of transfer learning, several recent works have used the idea of knowledge transfer to address challenges of SAR domains [151; 149; 154; 155; 156; 153].”
• SAR Image Classification Using Few-Shot Cross-Domain Transfer Learning (2019, background): “[12] address the problem of labeled data scarcity in the SAR domain via unsupervised learning.”
• Research Progress on Few-Shot Learning for Remote Sensing Image Interpretation (2021, methodology): “Huang et al. [48] investigated the effects of this method in SAR target recognition.”
• Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020, background): “It is stated that it is particularly successful even in few datasets [47].”