Summary of Hyperspectral Image Analysis in Single-modal and Multimodal Setting Using Deep Learning Techniques, by Shivam Pande
Hyperspectral Image Analysis in Single-Modal and Multimodal setting using Deep Learning Techniques
by Shivam Pande
First submitted to arxiv on: 3 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 paper proposes a novel deep learning approach to efficiently process hyperspectral imaging (HSI) data for land use and cover classification. The method integrates information from complementary modalities like LiDAR and SAR data through multimodal learning, and addresses issues due to domain disparities and missing modalities using adversarial learning and knowledge distillation. To handle the continuous spectral dimension of HSI data, the approach utilizes 1D convolutional and recurrent neural networks. Techniques like visual attention and feedback connections enhance feature extraction robustness. Self-supervised learning methods, including autoencoders for dimensionality reduction and semi-supervised techniques that leverage unlabeled data, are used to tackle limited training samples. The proposed approaches outperform existing state-of-the-art techniques across various HSI datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computer vision techniques to improve the accuracy of land use and cover classification from satellite images. It combines information from different types of data, like 3D maps and radar images, to get a more complete picture. The approach is designed specifically for these kinds of images, which have a lot of continuous data points. The method is tested on several sets of real-world data and performs better than other current approaches. |
Keywords
* Artificial intelligence * Attention * Classification * Deep learning * Dimensionality reduction * Feature extraction * Knowledge distillation * Self supervised * Semi supervised
