Adaptive boosting for SAR automatic target recognition

By Yijun Sun, Zhipeng Liu, Sinisa Todorovic, Jian Li. IEEE Transactions on Aerospace and Electronic Systems, 2007

341 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/648a65ea247a9fb8b359d2aa0dae5677373f0f31

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. Feng Xu
2. Haipeng Wang
3. Yaqiu Jin
4. Sizhe Chen
5. Jianwei Ma
6. Siwei Yu
7. Mohammad Rostami
8. Lichao Mou

In Computer Vision and Pattern Recognition

1. Khalid El-Darymli
2. Feng Xu
3. Eric W. Gill
4. Cecilia R. Moloney
5. Desmond Power
6. Hongwei Liu
7. Bo Chen
8. Jun Ding

Citations in the corpus

• Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review (2016, methodology): “A representative example for ensemble learning in SAR-ATR using AdaBoost is presented in [99].”
• SAR Automatic Target Recognition Based on Multiview Deep Learning Framework (2018, neutral): “These meth-ods are support vector machine (SVM) [12], adaptive boosting (AdaBoost) [45], conditional Gaussian model (CGM) [46], iterative graph thickening (IGT) [15], and two recently published DCNNs [23], [24].”
• Convolutional Neural Network With Data Augmentation for SAR Target Recognition (2016, background): “The features for recognition in [2] are the magnitudes of the 2-D DFT coefficients of the cropped images.”
• Target Classification Using the Deep Convolutional Networks for SAR Images (2016, methodology): “These methods include extended maximum average correlation height (EMACH) [29], SVM [9], conditional Gaussian model (Cond Gauss) [30], AdaBoost [10], iterative graph thickening (IGT) [31], sparse representation of monogenic signal (MSRC) [32], monogenic scale space (MSS) [33], and modified po-”