Summary of Unveiling the Power Of Wavelets: a Wavelet-based Kolmogorov-arnold Network For Hyperspectral Image Classification, by Seyd Teymoor Seydi and Zavareh Bozorgasl and Hao Chen
Unveiling the Power of Wavelets: A Wavelet-based Kolmogorov-Arnold Network for Hyperspectral Image Classification
by Seyd Teymoor Seydi, Zavareh Bozorgasl, Hao Chen
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel architecture, Wavelet-based Kolmogorov-Arnold Network (Wav-KAN), for efficient modeling of complex spatial-spectral correlations in hyperspectral image classification. Wav-KAN uses wavelet functions as learnable activation functions, allowing it to capture multi-scale patterns and outperform traditional multilayer perceptrons (MLPs) and Spline-based KAN (Spline-KAN). The model is evaluated on three benchmark datasets, demonstrating its superior performance. To further validate the generalizability of Wav-KAN, additional experiments are conducted on four more hyperspectral datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper develops a new way to classify images taken from satellites or planes that capture lots of information about the earth’s surface. The new method is called Wavelet-based Kolmogorov-Arnold Network (Wav-KAN). It works better than some other methods and can even work with really high-quality data. The researchers tested it on four different sets of images and found that it does a great job. |
Keywords
» Artificial intelligence » Image classification
