Sample Selection with SOMP for Robust Basis Recovery in Sparse Coding Dictionary Learning
| Autor: | Peter W. T. Yuen, Ayan Chatterjee |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Hyperspectral imaging
Pixel sparse coding Computer science business.industry Estimator Dictionary learning Pattern recognition Object detection Pearson product-moment correlation coefficient symbols.namesake symbols Trigonometric functions Artificial intelligence Neural coding business Linear combination |
| Zdroj: | IEEE Letters of the Computer Society. 2:28-31 |
| ISSN: | 2573-9689 |
| DOI: | 10.1109/locs.2019.2938446 |
| Popis: | Sparse Coding Dictionary (SCD) learning is to decompose a given hyperspectral image into a linear combination of a few bases. In a natural scene, because there is an imbalance in the abundance of materials, the problem of learning a given material well is directly proportional to its abundance in the training scene. By a random selection of pixels to train a given dictionary, the probability of bases learning a given material is proportional to its distribution in the scene. We propose to use SOMP residue for sample selection with each iteration for a more robust or ‘more complete’ learning. Experiments show that the proposed method learns from both background and trace materials accurately with over 0.95 in Pearson correlation coefficient. Furthermore, the proposed implementation has resulted in considerable improvements in Target Detection with Adaptive Cosine Estimator (ACE). |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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