Name
Title | ||
|---|---|---|
Learning Deep Spectral Features for Hyperspectral Data Using Convolution Over Spectral Signature Shape |
Abstract | ||
|---|---|---|
Deep convolutional neural networks learn the spatial image features automatically, for classifying a hyperspectral image. Learning the spectral features automatically is equally important in analyzing the hyperspectral image. However, most of the earlier work treat a hyperspectral pixel as a n dimensional vector (n = no. of bands) and a separate convolution is performed over the depth. The feature... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/WHISPERS52202.2021.9484018 | 2021 11th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS) |
Keywords | DocType | ISBN |
Convolution,Spectral shape,Shape,Semantics,Neural networks,Transforms,Image representation | Conference | 978-1-6654-3601-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shailesh Deshpande | 1 | 0 | 0.34 |
| Rohit Thakur | 2 | 0 | 0.34 |
| Balamuralidhar P | 3 | 10 | 5.25 |