Summary of 3d-convolution Guided Spectral-spatial Transformer For Hyperspectral Image Classification, by Shyam Varahagiri et al.
3D-Convolution Guided Spectral-Spatial Transformer for Hyperspectral Image Classification
by Shyam Varahagiri, Aryaman Sinha, Shiv Ram Dubey, Satish Kumar Singh
First submitted to arxiv on: 20 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV)
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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 The proposed 3D-Convolution guided Spectral-Spatial Transformer (3D-ConvSST) is a novel approach for Hyperspectral Image (HSI) classification that leverages the strengths of Vision Transformers (ViTs) and Convolutional Neural Networks (CNNs). By incorporating a 3D-Convolution Guided Residual Module (CGRM), the model effectively “fuses” local spatial and spectral information, enhancing feature propagation. Additionally, abandoning the class token and applying Global Average Pooling enables more discriminative high-level features for classification. The 3D-ConvSST outperforms state-of-the-art models on three public HSI datasets, demonstrating its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to classify Hyperspectral Images using a combination of transformers and convolutional neural networks. This helps computers recognize patterns in special images that have lots of information about what’s in the picture. The new method is tested on many different pictures and does better than other methods. |
Keywords
» Artificial intelligence » Classification » Token » Transformer
