Kavli Affiliate: Zhuo Li
| First 5 Authors: Chun Liu, Chun Liu, , ,
| Summary:
Open-set few-shot hyperspectral image (HSI) classification aims to classify
image pixels by using few labeled pixels per class, where the pixels to be
classified may be not all from the classes that have been seen. To address the
open-set HSI classification challenge, current methods focus mainly on
distinguishing the unknown class samples from the known class samples and
rejecting them to increase the accuracy of identifying known class samples.
They fails to further identify or discovery the unknow classes among the
samples. This paper proposes a prototype learning and clustering method for
discoverying unknown classes in HSIs under the few-shot environment. Using few
labeled samples, it strives to develop the ability of infering the prototypes
of unknown classes while distinguishing unknown classes from known classes.
Once the unknown class samples are rejected by the learned known class
classifier, the proposed method can further cluster the unknown class samples
into different classes according to their distance to the inferred unknown
class prototypes. Compared to existing state-of-the-art methods, extensive
experiments on four benchmark HSI datasets demonstrate that our proposed method
exhibits competitive performance in open-set few-shot HSI classification tasks.
All the codes are available at hrefhttps://github.com/KOBEN-ff/OpenFUCD-main
https://github.com/KOBEN-ff/OpenFUCD-main
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