Name
Title | ||
|---|---|---|
Ship Detection In Polarimetric Sar Images Via Tensor Robust Principle Component Analysis |
Abstract | ||
|---|---|---|
In order to avoid the disadvantages of CFAR detector and make full use of the polarimetric information, a novel method is proposed in this paper for detecting ships of polarimetric SAR images, based on tensor robust principle component analysis (tensor RPCA). This method is completely different from the traditional CFAR detector, and distribution model and sliding window are unnecessary. The polarimetric SAR image is firstly depicted by a tensor, then an improved version of tensor RPCA is applied to the tensor by using accelerated proximal gradient (APG) algorithm. For comparison, the polarimetric whitening filter (PWF) method is also used. Experiment results show that the proposed method has excellent performance. |
Year | Venue | Keywords |
|---|---|---|
2015 | 2015 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | polarimetric synthetic aperture radar, tensor RPCA, ship detection, low rank, sparse |
Field | DocType | ISSN |
Computer vision,Sliding window protocol,Polarimetry,Tensor,Computer science,Synthetic aperture radar,Remote sensing,Stress (mechanics),Robustness (computer science),Artificial intelligence,Detector,Sparse matrix | Conference | 2153-6996 |
Citations | PageRank | References |
2 | 0.41 | 3 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shengli Song | 1 | 2 | 0.41 |
| Jian Yang | 2 | 3 | 2.16 |