Graph inductive learning method for small sample classification of hyperspectral remote sensing images
| Autor: | Xibing Zuo, Xuchu Yu, Bing Liu, Pengqiang Zhang, Xiong Tan, Xiangpo Wei |
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| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: | |
| Zdroj: | European Journal of Remote Sensing, Vol 53, Iss 1, Pp 349-357 (2020) |
| Druh dokumentu: | article |
| ISSN: | 2279-7254 22797254 |
| DOI: | 10.1080/22797254.2021.1901064 |
| Popis: | In recent years, deep learning has drawn increasing attention in the field of hyperspectral remote sensing image classification and has achieved great success. However, the traditional convolutional neural network model has a huge parameter space, in order to obtain a better classification model, a large number of labeled samples are often required. In this paper, a graph induction learning method is proposed to solve the problem of small sample in hyperspectral image classification. It treats each pixel of the hyperspectral image as a graph node and learns the aggregation function of adjacent vertices through graph sampling and graph aggregation operations to generate the embedding vector of the target vertex. Experimental results on three well-known hyperspectral data sets show that this method is superior to the current semi-supervised methods and advanced deep learning methods. |
| Databáze: | Directory of Open Access Journals |
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