论文标题
使用具有形状自适应重建的SVM对高光谱图像进行分类和平滑的总变化
Classification of Hyperspectral Images Using SVM with Shape-adaptive Reconstruction and Smoothed Total Variation
论文作者
论文摘要
在这项工作中，引入了一种称为SVM的新型算法，具有形状适应性重建和平滑的总变异（SAR-SVM-STV），以对高光谱图像进行分类，从而充分利用空间和光谱信息。基于其形状自适应（SA）区域的像素之间的Pearson相关性，引入了形状自适应重建（SAR）以预处理每个像素。培训支持向量机（SVM），以估算每个类别的像素概率图。然后将平滑的总变化（STV）模型应用于Deoise并生成最终分类图。实验表明，SAR-SVM-STV的表现优于SVM-STV方法，其中一些训练标签表明在分类前重建高光谱图像的重要性。
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.
