SPN: Stable Prototypical Network for Few-Shot Learning-Based Hyperspectral Image Classification
| Autor: | Yogananda Jeppu, Valay Bundele, Biplab Banerjee, Debabrata Pal |
|---|---|
| Rok vydání: | 2022 |
| Předmět: |
Contextual image classification
business.industry Computer science Hyperspectral imaging Pattern recognition Context (language use) Overfitting Geotechnical Engineering and Engineering Geology Term (time) Feature (computer vision) Benchmark (computing) Artificial intelligence Electrical and Electronic Engineering business Dropout (neural networks) |
| Zdroj: | IEEE Geoscience and Remote Sensing Letters. 19:1-5 |
| ISSN: | 1558-0571 1545-598X |
| DOI: | 10.1109/lgrs.2021.3085522 |
| Popis: | We tackle the problem of few-shot image classification in the context of remote sensing hyperspectral images (HSIs). Due to the difficulties in collecting a large number of labeled training samples, the few-shot classification techniques hold much prominence in remote sensing in general. One of the bottlenecks in designing few-shot learning (FSL) systems arises from the fact that the model is likely to overfit in the presence of few training samples and the complex spectral feature distributions of the land-cover classes. To this end, we introduce a stable prototypical network (SPN) for FSL by judiciously incorporating dropout and DropBlock-based regularizers jointly within the framework and averaging model parameters using the Monte Carlo approximation. Besides, a novel variance loss term to reduce the uncertainty of the network is considered together with the cross-entropy-based classification loss to train the model in an end-to-end manner. The experimental analysis on three benchmark HSI datasets confirms the SPN's superior performance. |
| Databáze: | OpenAIRE |
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