| Autor: |
CHEN Shanxue, WANG Xinxin |
| Předmět: |
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| Zdroj: |
Systems Engineering & Electronics; Sep2021, Vol. 43 Issue 9, p2422-2429, 8p |
| Abstrakt: |
Aiming at the situation of insufficient utilization of spectral information or spatial information and low classification accuracy in hyperspectral image classification, a joint sparse representation classification method combined with spatial preprocessing is proposed. On the one hand, it can make up for the problem of insufficient utilization of spatial information in the fixed window mode of the joint sparse representation; on the other hand, it also avoids the pixels from participating in the construction process of the joint sparse model multiple times. Considering that each pixel contributes differently to the joint sparse model, the neighboring pixels are given corresponding weights to improve the accuracy of sparse reconstruction. Finally, make full use of the known information of the training samples to correct the classification results. Experiments were conducted on Pavia University and AVIRIS Salinas. The experimental results show that the method proposed in this paper can effectively improve the classification accuracy of hyperspectral images. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
| Externí odkaz: |
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