Abstract | ||
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This letter presents sparse coding for interferometric synthetic aperture radar (InSAR) patch categorization. Motivated by the fact that an optimal dual based l1 analysis can achieve better recognition rates, this letter proposes sparse coding with optimal dual-based l1 analysis, which is applied to the amplitude and phase of the InSAR patches. The minimization of cost functions for amplitude and ... |
Year | DOI | Venue |
---|---|---|
2017 | 10.1109/LGRS.2017.2689506 | IEEE Geoscience and Remote Sensing Letters |
Keywords | Field | DocType |
Encoding,Dictionaries,Synthetic aperture radar,Algorithm design and analysis,Image coding,Minimization,Data models | Computer vision,Gradient descent,Interferometric synthetic aperture radar,Algorithm design,Bag-of-words model in computer vision,Computer science,Synthetic aperture radar,Neural coding,Minification,Artificial intelligence,Amplitude | Journal |
Volume | Issue | ISSN |
14 | 6 | 1545-598X |
Citations | PageRank | References |
0 | 0.34 | 14 |
Authors | ||
2 |
Name | Order | Citations | PageRank |
---|---|---|---|
P. Planinšič | 1 | 27 | 7.70 |
Du¿an Gleich | 2 | 95 | 7.17 |