Explainable Two-Layer Mode Machine Learning Method for Hyperspectral Image Classification
Explainable machine learning methods with a specific mathematical model provide insights into how the model works. We propose a new mode that contains a two-layer architecture for hyperspectral image (HSI) classification. In the front-end learning layer, superpixel segmentation and mathematical mode...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/11/5859 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Explainable machine learning methods with a specific mathematical model provide insights into how the model works. We propose a new mode that contains a two-layer architecture for hyperspectral image (HSI) classification. In the front-end learning layer, superpixel segmentation and mathematical models are combined to achieve the band selection, which obtains the data re-expression in a lower dimension. The mathematical model uses the <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msub><mrow><mi>l</mi></mrow><mrow><mn>2,1</mn></mrow></msub></mrow></semantics></math></inline-formula> norm and graph regularized term, which helps induce sparsity, improve robustness to outliers and noise, and enhance the explainability of the data re-expression. We employ the support vector machine or the K-nearest neighbor algorithms in the back-end layer to classify low-dimensional data. Finally, the two-layer mode classification method is applied to the three real HSI dataset classifications. Numerical results show that the overall classification accuracy of our method is improved. |
|---|---|
| ISSN: | 2076-3417 |