Name
Abstract | ||
|---|---|---|
This paper presents a novel approach for automatic target recognition (ATR) using inverse synthetic aperture radar (ISAR) images. This proposed approach is mainly composed of two steps. In the first step, we adopt a statistical method to compute a novel target template from feature descriptors. The proposed template is achieved by combining the Gamma statistical parameters of the both dual-tree complex wavelet transform (DT-CWT) coefficients and the scale-invariant feature transform (SIFT) descriptor. In order to validate the proposed target template, we achieve in the second step the recognition task using a sparse representation-based classification (SRC) method. The performance of the proposed approach has been successfully verified using ISAR images reconstructed from anechoic chamber. The experimental results show that the proposed method can achieve a high average accuracy and is significantly superior to the well-known SVM classifier. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1145/3010089.3010134 | Proceedings of the International Conference on Big Data and Advanced Wireless Technologies |
Field | DocType | ISBN |
Statistical parameter,Scale-invariant feature transform,Computer science,Anechoic chamber,Artificial intelligence,Computer vision,Pattern recognition,Automatic target recognition,Sparse approximation,Inverse synthetic aperture radar,Statistical model,Complex wavelet transform,Machine learning | Conference | 978-1-4503-4779-2 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ayoub Karine | 1 | 0 | 0.34 |
| Abdelmalek Toumi | 2 | 22 | 9.24 |
| ali khenchaf | 3 | 98 | 30.12 |
| Mohammed El Hassouni | 4 | 135 | 29.52 |