论文标题
无监督的高光谱图像分割的分布依赖于分布的mumford-shah模型
A distribution-dependent Mumford-Shah model for unsupervised hyperspectral image segmentation
论文作者
论文摘要
高光谱图像为每个像素提供了丰富的基础频谱的表示，从而使像素的分类/分割分为不同的类别。由于获得标记的培训数据的获取非常耗时，因此无监督的方法在高光谱图像分析中至关重要。高光谱数据中的频谱变异性和噪声使此任务非常具有挑战性，并定义了此类方法的特殊要求。 在这里，我们提出了一个新颖的无监督的高光谱分割框架。它首先是降低和尺寸降低的步骤，从建立的最小噪声分数（MNF）变换开始。然后，将Mumford-Shah（MS）分割函数应用于分段数据。我们为MS功能配备了一种新型的鲁棒分布依赖性指标函数，旨在处理高光谱数据的特征挑战。为了优化我们的目标函数，相对于无封闭形式解决方案的参数，我们提出了有效的固定点迭代方案。四个公共基准数据集的数值实验表明，我们的方法会产生竞争结果，在这些数据集中的三个数据集上大大优于三种最新方法。
Hyperspectral images provide a rich representation of the underlying spectrum for each pixel, allowing for a pixel-wise classification/segmentation into different classes. As the acquisition of labeled training data is very time-consuming, unsupervised methods become crucial in hyperspectral image analysis. The spectral variability and noise in hyperspectral data make this task very challenging and define special requirements for such methods. Here, we present a novel unsupervised hyperspectral segmentation framework. It starts with a denoising and dimensionality reduction step by the well-established Minimum Noise Fraction (MNF) transform. Then, the Mumford-Shah (MS) segmentation functional is applied to segment the data. We equipped the MS functional with a novel robust distribution-dependent indicator function designed to handle the characteristic challenges of hyperspectral data. To optimize our objective function with respect to the parameters for which no closed form solution is available, we propose an efficient fixed point iteration scheme. Numerical experiments on four public benchmark datasets show that our method produces competitive results, which outperform three state-of-the-art methods substantially on three of these datasets.
