Name
Title | ||
|---|---|---|
SAR patch categorization using dual tree orientec wavelet transform and stacked autoencoder |
Abstract | ||
|---|---|---|
This paper presents a categorization of Synthetic Aperture Radar (SAR) data patches. The categories of the SAR data were designed manually by cutting several spotlight SAR products into different categories. The supervised approach to the categorization was proposed, where an oriented dual tree wavelet transform was used to decompose energy of the original image. Subbands of wavelet transforms with different orientations were used for computation of spectral features. The log commulants were estimated for each subband and 8 additional rotations were used for feature extraction. Those features were fed into stacked autoencoder (SAE). The SAE was pre-trained by greedy layer-wise training method. Capable of feature expression, SAE makes the fused features more distinguishable. Finally, the model is fine-tuned by a softmax classifier and applied to the categories selection of targets. The proposed method is comparable with the state-of-the art methods for SAR data categorization. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/IWSSIP.2017.7965615 | 2017 International Conference on Systems, Signals and Image Processing (IWSSIP) |
Keywords | Field | DocType |
Synthetic Aperture Radar,oriented dual tree wavelet transform,neural network,stacked autoencoder | Computer vision,Categorization,Autoencoder,Pattern recognition,Softmax function,Synthetic aperture radar,Computer science,Feature extraction,Artificial intelligence,Classifier (linguistics),Wavelet packet decomposition,Wavelet transform | Conference |
ISSN | ISBN | Citations |
2157-8672 | 978-1-5090-6345-1 | 0 |
PageRank | References | Authors |
0.34 | 20 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Du¿an Gleich | 1 | 95 | 7.17 |
| P. Planinšič | 2 | 27 | 7.70 |