Name
Title | ||
|---|---|---|
Convolutional Neural Networks For Heterogeneous Ingredient Discrimination With Hyperspectral Imaging |
Abstract | ||
|---|---|---|
Convolutional Neural Networks (CNNs) are recently gaining popularity to perform a joint spatio-spectral analysis of hyperspectral images and have achieved good performance in remote sensing applications. We show the potential of CNNs for an industrial application of heterogeneous ingredient detection and show a significant discrimination gain with respect to traditional machine learning methods. Additionally, we explore the potential of using down-sampled spatio-spectral resolutions of the hyperspectral image achieving high discrimination while reducing data storage, acquisition and computational requirements. Finally, we show how CNNs can enable the use of low-resolution snapshot cameras, which allow portability and fast acquisition in industrial applications. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/WHISPERS.2019.8921395 | 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) |
Keywords | Field | DocType |
Convolutional neural network,spatio-spectral resolution,ingredient identification | Pattern recognition,Convolutional neural network,Computer science,Computer data storage,Ingredient,Hyperspectral imaging,Remote sensing application,Software portability,Artificial intelligence,Snapshot (computer storage),Image resolution | Conference |
ISSN | ISBN | Citations |
2158-6268 | 978-1-7281-5295-0 | 0 |
PageRank | References | Authors |
0.34 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carolina Blanch Perez del Notario | 1 | 0 | 0.34 |
| Wouter Saeys | 2 | 78 | 11.04 |
| Andy Lambrechts | 3 | 173 | 12.73 |