# Classification of Hyperspectral Images Using SVM with Shape-adaptive Reconstruction and Smoothed Total Variation

arXiv: https://arxiv.org/abs/2203.15619 · HTML: https://mlsanity.com/paper/2203.15619/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion](https://mlsanity.com/paper/1704.07961/) (2017)
- [A Three-stage Approach for Segmenting Degraded Color Images: Smoothing, Lifting and Thresholding (SLaT)](https://mlsanity.com/paper/1506.00060/) (2015)
- [A Multiscale Environment for Learning by Diffusion](https://mlsanity.com/paper/2102.00500/) (2021)
- [A two-stage method for spectral-spatial classification of hyperspectral images](https://mlsanity.com/paper/1806.00836/) (2018)

## Built on by (most cited first, 4 of 4)

- [Superpixel-based and Spatially-regularized Diffusion Learning for Unsupervised Hyperspectral Image Clustering](https://mlsanity.com/paper/2312.15447/) (2023)
- [A 3-stage Spectral-spatial Method for Hyperspectral Image Classification](https://mlsanity.com/paper/2204.09294/) (2022)
- [Semi-supervised Change Detection of Small Water Bodies Using RGB and Multispectral Images in Peruvian Rainforests](https://mlsanity.com/paper/2206.09365/) (2022)
- [Unsupervised Spatial-spectral Hyperspectral Image Reconstruction and Clustering with Diffusion Geometry](https://mlsanity.com/paper/2204.13497/) (2022)

## Abstract (arXiv)

In this work, a novel algorithm called SVM with Shape-adaptive Reconstruction and Smoothed Total Variation (SaR-SVM-STV) is introduced to classify hyperspectral images, which makes full use of spatial and spectral information. The Shape-adaptive Reconstruction (SaR) is introduced to preprocess each pixel based on the Pearson Correlation between pixels in its shape-adaptive (SA) region. Support Vector Machines (SVMs) are trained to estimate the pixel-wise probability maps of each class. Then the Smoothed Total Variation (STV) model is applied to denoise and generate the final classification map. Experiments show that SaR-SVM-STV outperforms the SVM-STV method with a few training labels, demonstrating the significance of reconstructing hyperspectral images before classification.