Name
Abstract | ||
|---|---|---|
In this paper we present a novel method for robust hyperspectral image classification using context and rejection. Hyper spectral image classification is generally an ill-posed image problem where pixels may belong to unknown classes, and obtaining representative and complete training sets is costly. Furthermore, the need for high classification accuracies is frequently greater than the need to classify the entire image.We approach this problem with a robust classification method that combines classification with context with classification with rejection. A rejection field that will guide the rejection is derived from the classification with contextual information obtained by using the SegSALSA [1] algorithm. We validate our method in real hyperspectral data and show that the performance gains obtained from the rejection fields are equivalent to an increase the dimension of the training sets. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/WHISPERS.2015.8075465 | 2015 7TH WORKSHOP ON HYPERSPECTRAL IMAGE AND SIGNAL PROCESSING: EVOLUTION IN REMOTE SENSING (WHISPERS) |
Keywords | Field | DocType |
Hyperspectral image classification, hidden fields, robust classification, classification with rejection | Hyperspectral image classification,Computer vision,Contextual information,Pattern recognition,Computer science,Hyperspectral imaging,Artificial intelligence,Pixel,Machine learning | Journal |
Volume | ISSN | Citations |
abs/1504.07918 | 2158-6268 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Filipe Condessa | 1 | 20 | 4.52 |
| José M. Bioucas-Dias | 2 | 3565 | 173.67 |
| Jelena Kovacevic | 3 | 802 | 95.87 |