Tree Species Classification on Hyperspectral Imagery Using Fewer Training Samples
| Autor: | F. Tong, Y. Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XLVIII-M-4-2024, Pp 71-76 (2024) |
| Druh dokumentu: | article |
| ISSN: | 1682-1750 2194-9034 |
| DOI: | 10.5194/isprs-archives-XLVIII-M-4-2024-71-2024 |
| Popis: | The distribution of tree species within the forest holds significant importance for forest management. Since field surveys in the forest are time-consuming and cost-expensive, automatically extracting tree species distribution maps from remote sensing imagery becomes a trend. For tree species classification using hyperspectral imagery, many existing classification methods require a large number of training samples to achieve high classification accuracy. However, the classification accuracy will decrease rapidly if only a few hundred training samples are used. Given the challenges and expenses associated with collecting abundant training samples in the forest, there is a need to explore methods that achieve good classification performance with a limited number of training samples. In this paper, a classification scheme combining SuperPCA and Active Learning (AL) is proposed to improve the tree species classification using a limited number of training samples. SuperPCA is employed to reduce feature dimensions and harness spectral-spatial information within hyperspectral imagery. Active Learning is employed to select informative samples for the training, thus reducing the requirement for training samples. Experiments on a tree species classification data set demonstrate the effectiveness of the proposed classification scheme. |
| Databáze: | Directory of Open Access Journals |
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