SAR target recognition based on deep learning
Abstract
Deep learning algorithms such as convolutional neural networks (CNN) have been successfully applied in computer vision. This paper attempts to adapt the optical camera-oriented CNN to its microwave counterpart, i.e. synthetic aperture radar (SAR). As a preliminary study, a single layer of convolutional neural network is used to automatically learn features from SAR images. Instead of using the classical backpropagation algorithm, the convolution kernel is trained on randomly sampled image patches using unsupervised sparse auto-encoder. After convolution and pooling, an input SAR image is then transformed into a series of feature maps. These feature maps are then used to train a final softmax classifier. Initial experiments on MSTAR public data set show that an accuracy of 90.1% can be achieved on three types of targets classification task, and an accuracy of 84.7% is achievable on ten types of targets classification task.
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With machine learning techniques, vibrant progress has been made in incorporating human-level reasoning into traditional satellite image models.
“With ML techniques vibrant progress has been made in incorporating human-level reasoning into these traditional models [157].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [157]
Giorgio Giacinto et al. proposed an approach combining neural and statistical algorithms in a simple design phase, disclosing that every algorithm is significant in solving a particular issue, and no single algorithm is proved perfect.
“In [157], Giorgio Giacinto et al . proposed an approach with a combination of neural and statistical algorithms in a simple design phase to disclose that every algorithm is significant in solving a particular issue, and no single algorithm is proved to be perfect.”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [157]
The development of the parallel processing method is still in progress.
“However, the development of this method is still in progress [157].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [157]
Complexities associated with training deep networks with a vast number of hidden layers can be resolved to a greater extent by employing a greedy learning-based algorithm.
“Complexities associated with the training of deep networks with a vast number of hidden layers can be resolved to a greater extent by employing a greedy learning-based algorithm [157].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [157]
Pabitra Mitra et al. combined an active learning method with SVM and advanced an active SVM with reduced labeled data for classification.
“In [169], Pabitra Mitra et al . combined an active learning method with SVM and advanced an active SVM with reduced labeled data for classification [157].”
From Applications and Challenges of Artificial Intelligence in Space Missions (2024) · cites this paper as [157]
Cites
ImageNet classification with deep convolutional neural networks
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What is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153 · see citing works
Multi-column deep neural network for traffic sign classification
Cireşan, D., Meier, U., Masci, J., & Schmidhuber, J. (2012) · Neural Networks · doi:10.1016/j.neunet.2012.02.023 · Cited by 1,057 · see citing works
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Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources
Zhu, X. X., Tuia, D., Mou, L., Xia, G.-S., Zhang, L., Xu, F., & Fraundorfer, F. (2017) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2017.2762307 · Cited by 3,400 · see citing works
Ball, J. E., Anderson, D. T., & Chan, C. S. (2017) · Journal of Applied Remote Sensing · doi:10.1117/1.jrs.11.042609 · Cited by 746 · see citing works
Applications and Challenges of Artificial Intelligence in Space Missions
Oche, P. A., Ewa, G. A., & Ibekwe, N. (2024) · IEEE Access · doi:10.1109/access.2021.3132500 · Cited by 68 · see citing works
“As a preliminary study, Chen and Wang in [155] used a single layer of Convolutional Neural Network (CNN) to learn features from SAR images automatically.”
Method
- Authors
- Chen, S., & Wang, H.
- Affiliations
- Fudan University (CN)
- Venue
- 2014 International Conference on Data Science and Advanced Analytics (DSAA)
- Publisher
- IEEE
- Published
- 2014-10
- Pages
- 541-547
- OpenAlex
- W2544339223
- Cited by
- 224
- References
- 3
- Citations by year
- 2026: 4 · 2025: 21 · 2024: 18 · 2023: 20 · 2022: 32 · 2021: 29 · 2020: 23 · 2019: 28 · 2018: 25 · 2017: 19 · 2016: 4 · 2015: 1
- FWCI
- 13.47
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Data Science and Advanced Analytics (DSAA) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Aerospace Engineering › Advanced SAR Imaging Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Engineering › Media Technology › Remote-Sensing Image Classification
- Keywords
- synthetic aperture radar, SAR target recognition, convolutional neural networks, feature extraction, MSTAR dataset, target classification, microwave imaging
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