Support vector machines for SAR automatic target recognition

By Qun Zhao, José Carlos Príncipe. 2001

545 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/e6acdefcae231904f22d7fb71e900f1de2e78659

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. Xiaoxiang Zhu
2. Richard Bamler
3. Lichao Mou
4. Feng Xu
5. Haipeng Wang
6. Yaqiu Jin
7. Feng Xu
8. Sizhe Chen

In Computer Vision and Pattern Recognition

1. Lichao Mou
2. Xiaoxiang Zhu
3. Feng Xu
4. Richard Bamler
5. Mohsin Ali
6. Sizhe Chen
7. Haipeng Wang
8. Yuanyuan Wang

In Media Technology

1. Xiaoxiang Zhu
2. Lichao Mou
3. Feng Xu
4. Richard Bamler
5. Sizhe Chen
6. Haipeng Wang
7. Yuanyuan Wang
8. Feng Xu

Citations in the corpus

• Target Classification Using the Deep Convolutional Networks for SAR Images (2016, methodology): “In [35], using the same experimental scenario, at Pd = 90%, the Pfa of four SAR-ATR algorithms (template matching, principal component analysis with multiresolution method, neural network with quadratic mutual information cost function, and SVM with Gaussian kernels) are equal to 46.5%, 40%, 45.5%, ”
• SAR Automatic Target Recognition Based on Multiview Deep Learning Framework (2018, background): “In recent years, many novel SAR ATR algorithms and systems have been designed and proposed, and most of them have performed well in applications [12]–[16].”
• Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review (2016, unknown): “Representative examples of the SVM classifier are given in [86] and [87].”
• Robust method for the matching of attributed scattering centers with application to synthetic aperture radar automatic target recognition (2016, methodology): “Step (3) Reconstruct the image by the estimated ASCs and subtract the reconstructed image from the target region using CLEAN algorithm.”
• Deep Learning Meets SAR: Concepts, models, pitfalls, and perspectives (2020, methodology): “through the use of traditional machine learning approaches, such as Support Vector Machines (SVMs) [13], [14]; in contrast, modern deep learning algorithms aim at applying deep CNNs to extract discriminative features automatically for target recognition [7].”