Name
Title | ||
|---|---|---|
Semi-supervised sparse representation classifier (S3RC) with deep features on small sample sized hyperspectral images |
Abstract | ||
|---|---|---|
•A novel deep learning based framework is proposed for hyperspectral images with using both labeled and unlabeled samples for small sample sized images.•The main aim of the study is constructing a general purpose deep model for a specific hyperspectral sensor type and using the model with little effort for all data sets obtained from this sensor type.•Exploiting transfer learning strategy in semi-supervised hyperspectral image classification.•A dictionary based semi-supervised learning method, namely Semi-Supervised Sparse Representation Classifier (S3RC), is proposed for small sample sized data sets.•Linear separability and reduced size of deep features increase the classification accuracy while decreasing the computational complexity. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.neucom.2020.02.092 | Neurocomputing |
Keywords | DocType | Volume |
Hyperspectral images,Deep learning,Transfer learning,Semi-supervised learning,Sparse classifier | Journal | 399 |
ISSN | Citations | PageRank |
0925-2312 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Aydemir, M.Said | 1 | 4 | 1.40 |
| Gökhan Bilgin | 2 | 62 | 13.18 |