Name
Title | ||
|---|---|---|
POLSAR Target Recognition Using a Feature Fusion Framework Based on Monogenic Signal and Complex-Valued Nonlocal Network |
Abstract | ||
|---|---|---|
With the continuous development of synthetic aperture radar (SAR) systems, multipolarization information has been increasingly applied to numerous fields, and automatic target recognition (ATR) in polarimetric SAR (POLSAR) has been recognized as vital problem. The SAR recognition methods can primarily fall into handcrafted feature-based algorithms and deep learning algorithms. The former exhibits excellent interpretability but insufficient generalization; the latter achieves stronger representational ability but relies on a considerable number of samples. To solve above problems, a feature fusion framework is proposed in this article based on monogenic signal and complex-valued nonlocal network (CVNLNet) for POLSAR target recognition. The proposed feature fusion framework effectively uses the complementarity of handcrafted features and deep features, while making up for the disadvantages of single feature-based methods. First, a Mono-BOVW model is proposed based on monogenic signal and bag-of-visual-words (BOVW) model to extract handcrafted features, which can more fully mine the information covered in POLSAR data in multiscale space. Moreover, CVNLNet is built for deep feature extraction to use both the amplitude and phase covered in POLSAR data. Next, a kernel discrimination correlation analysis algorithm is proposed to jointly analyze and transform the two features, so as to remove redundant information while retaining effective and discriminative information. Experiments on the MSTAR dataset and the GOTCHA dataset show that the proposed framework has superior performance on single polarimetric and fully polarimetric datasets. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/JSTARS.2022.3194551 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING |
Keywords | DocType | Volume |
Feature extraction, Synthetic aperture radar, Target recognition, Correlation, Radar polarimetry, Radar, Transforms, Complex-valued non-local network (CVNLNet), feature fusion, monogenic signal, polarimetric synthetic aperture radar (POLSAR), target recognition | Journal | 15 |
ISSN | Citations | PageRank |
1939-1404 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Feng Li | 1 | 34 | 11.31 |
| Min Yi | 2 | 0 | 0.34 |
| Chaoqi Zhang | 3 | 0 | 0.34 |
| Weijun Yao | 4 | 0 | 0.34 |
| Xueyao Hu | 5 | 0 | 0.34 |
| Zhipeng Liu | 6 | 36 | 6.61 |