This article presents a label similarity probability filter (LSPF) to make hyperspectral image postclassification. The LSPF is inspired by the first law of geography and proposes a class label probability function to quantify the probability of both centered and its neighboring pixels belonging to the same class. It first classifies the hyperspectral data using the regular support vector machine classifier. Then, it binarizes the posterior classification result to obtain the binary label maps of each class. After that, it traverses all spatial windows centered by each pixel and calculates the cumulative probability of all pixels in each class. Finally, the cumulative probabilities are used to make reclassification to obtain the refined classification map. The experiments on Indian Pines, Pavia University, and ZY1-02D Yellow River Estuary data show that LSPF greatly improves the classification accuracy of spectral signatures and outperforms other state-of-the-art spectral–spatial methods.

中文翻译：

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用于高光谱图像后分类的标签相似性概率滤波器

本文提出了一种标签相似性概率滤波器 (LSPF) 来进行高光谱图像后分类。LSPF 受到地理学第一定律的启发，提出了一个类标签概率函数来量化中心像素及其相邻像素属于同一类的概率。它首先使用常规支持向量机分类器对高光谱数据进行分类。然后，对后验分类结果进行二值化，得到每个类的二值标签图。之后，它遍历以每个像素为中心的所有空间窗口，并计算每个类中所有像素的累积概率。最后，利用累积概率进行重分类，得到细化的分类图。在印度松树上的实验，帕维亚大学，