# New SAR target recognition based on YOLO and very deep multi-canonical correlation analysis

arXiv: https://arxiv.org/abs/2110.15383 · HTML: https://mlsanity.com/paper/2110.15383/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 39 references that are in the map.
- First posted on arXiv 28 Oct 2021.

## Builds on (most cited first, 7 of 7)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Multi-digit Number Recognition from Street View Imagery using Deep Convolutional Neural Networks](https://mlsanity.com/paper/1312.6082/) (2013)
- [Deep Learning using Linear Support Vector Machines](https://mlsanity.com/paper/1306.0239/) (2013)
- [Learning Low Dimensional Convolutional Neural Networks for High-Resolution Remote Sensing Image Retrieval](https://mlsanity.com/paper/1610.03023/) (2016)
- [Deep Learning for Target Classification from SAR Imagery: Data Augmentation and Translation Invariance](https://mlsanity.com/paper/1708.07920/) (2017)

## Built on by (most cited first, 5 of 5)

- [A Dual-Polarization Information Guided Network for SAR Ship Classification](https://mlsanity.com/paper/2207.04639/) (2022)
- [Exploring Syn-to-Real Domain Adaptation for Military Target Detection](https://mlsanity.com/paper/2512.23208/) (2025)
- [EMC2A-Net: An Efficient Multibranch Cross-channel Attention Network for SAR Target Classification](https://mlsanity.com/paper/2208.01836/) (2022)
- [Explainable, Physics Aware, Trustworthy AI Paradigm Shift for Synthetic Aperture Radar](https://mlsanity.com/paper/2301.03589/) (2023)
- [Human Gait Recognition using Deep Learning: A Comprehensive Review](https://mlsanity.com/paper/2309.10144/) (2023)

## Abstract (arXiv)

Synthetic Aperture Radar (SAR) images are prone to be contaminated by noise, which makes it very difficult to perform target recognition in SAR images. Inspired by great success of very deep convolutional neural networks (CNNs), this paper proposes a robust feature extraction method for SAR image target classification by adaptively fusing effective features from different CNN layers. First, YOLOv4 network is fine-tuned to detect the targets from the respective MF SAR target images. Second, a very deep CNN is trained from scratch on the moving and stationary target acquisition and recognition (MSTAR) database by using small filters throughout the whole net to reduce the speckle noise. Besides, using small-size convolution filters decreases the number of parameters in each layer and, therefore, reduces computation cost as the CNN goes deeper. The resulting CNN model is capable of extracting very deep features from the target images without performing any noise filtering or pre-processing techniques. Third, our approach proposes to use the multi-canonical correlation analysis (MCCA) to adaptively learn CNN features from different layers such that the resulting representations are highly linearly correlated and therefore can achieve better classification accuracy even if a simple linear support vector machine is used. Experimental results on the MSTAR dataset demonstrate that the proposed method outperforms the state-of-the-art methods.