We propose a novel two-stage method for the classification of hyperspectral images. Pixel-wise classifiers, such as the classical support vector machine (SVM), consider spectral information only. As spatial information is not utilized, the classification results are not optimal and the classified image may appear noisy. Many existing methods, such as morphological profiles, superpixel segmentation, and composite kernels, exploit the spatial information. In this paper, we propose a two-stage approach inspired by image denoising and segmentation to incorporate the spatial information. In the first stage, SVMs are used to estimate the class probability for each pixel. In the second stage, a convex variant of the Mumford–Shah model is applied to each probability map to denoise and segment the image into different classes. Our proposed method effectively utilizes both spectral and spatial information of the data sets and is fast as only convex minimization is needed in addition to the SVMs. Experimental results on three widely utilized real hyperspectral data sets indicate that our method is very competitive in accuracy, timing, and the number of parameters when compared with current state-of-the-art methods, especially when the inter-class spectra are similar or the percentage of training pixels is reasonably high.

中文翻译：

---

高光谱图像光谱空间分类的两阶段方法

我们提出了一种用于高光谱图像分类的新颖的两阶段方法。像素分类器（例如经典支持向量机（SVM））仅考虑频谱信息。由于未利用空间信息，因此分类结果不是最佳的，并且分类的图像可能看起来很吵。许多现有的方法，例如形态轮廓，超像素分割和复合核，都利用了空间信息。在本文中，我们提出了一种受图像去噪和分割启发的两阶段方法来合并空间信息。在第一阶段，支持向量机用于估计每个像素的分类概率。在第二阶段，将Mumford-Shah模型的凸变体应用于每个概率图，以对图像进行降噪并将其分割为不同的类别。我们提出的方法有效地利用了数据集的频谱和空间信息，并且由于支持向量机的原因，仅需要凸极小化，因此速度很快。在三个广泛使用的实际高光谱数据集上的实验结果表明，与当前的最新方法相比，尤其是当类间光谱相似或相似时，我们的方法在准确性，时序和参数数量方面具有极强的竞争力。训练像素的百分比相当高。