Classification of Hyperspectral Images Using SVM with Shape-adaptive Reconstruction and Smoothed Total Variation
This is an incremental improvement for hyperspectral image analysis, enhancing classification accuracy with limited data.
The authors tackled hyperspectral image classification by introducing SaR-SVM-STV, a method combining shape-adaptive reconstruction and smoothed total variation, which outperformed SVM-STV with few training labels.
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.