利用空间相关性增强高光谱图像分解
Enhancing hyperspectral image unmixing with spatial correlations
摘要：
提出了一种新的高光谱图像混合分解算法。文献中提出的大多数解混算法没有考虑像素之间可能存在的空间相关性。在这项工作中，贝叶斯模型被引入到利用这些相关性。假设要解混的图像被划分为丰度系数的统计特性是均匀的区域（或类）。马尔可夫随机场，然后提出了任何类内的像素的空间依赖性建模。在给定类的情况下，每个像素都使用加性高斯白噪声的经典线性混合模型来建模。这种策略是众所周知的线性混合模型。对于该模型，未知参数和超参数的后验分布允许人们推断感兴趣的参数。这些参数包括每个像素的丰度，每个类别的丰度的均值和方差，以及指示图像中所有像素的类别的分类图。为了克服感兴趣的后验分布的复杂性，我们考虑马尔可夫链蒙特卡罗方法，根据感兴趣的后验分布生成样本。然后，将生成的样本用于参数和超参数估计。所提出的算法的准确性说明合成和真实数据。
英文摘要：
This paper describes a new algorithm for hyperspectral image unmixing. Most of the unmixing algorithms proposed in the literature do not take into account the possible spatial correlations between the pixels. In this work, a Bayesian model is introduced to exploit these correlations. The image to be unmixed is assumed to be partitioned into regions (or classes) where the statistical properties of the abundance coefficients are homogeneous. A Markov random field is then proposed to model the spatial dependency of the pixels within any class. Conditionally upon a given class, each pixel is modeled by using the classical linear mixing model with additive white Gaussian noise. This strategy is investigated the well known linear mixing model. For this model, the posterior distributions of the unknown parameters and hyperparameters allow ones to infer the parameters of interest. These parameters include the abundances for each pixel, the means and variances of the abundances for each class, as well as a classification map indicating the classes of all pixels in the image. To overcome the complexity of the posterior distribution of interest, we consider Markov chain Monte Carlo methods that generate samples distributed according to the posterior of interest. The generated samples are then used for parameter and hyperparameter estimation. The accuracy of the proposed algorithms is illustrated on synthetic and real data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
