基于元胞自动机模型的遥感图像亚像元定位

Sub-pixel Mapping of Remote Sensing Images Based on Cellular Automata Model

由于遥感图像中普遍存在混合像元,因此传统分类方法得到的结果通常会存在较大误差,应用混合像元分解技术,虽然可以得到混合像元中各端元组分的丰度,但是却不能得到各端元组分的空间分布状态,而亚像元定位则是在混合像元分解的基础上,将混合像元剖分为亚像元,再利用端元组分的丰度及像元空间分布的特点,将亚像元赋予不同端元组分来得到各端元组分的空间分布情况,以提高遥感图像分类的精度。为了更好地解决亚像元定位问题,结合亚像元定位的理论模型,提出了一种新的元胞自动机模型,并通过模拟数据和实际数据对该模型进行了检验,结果表明,该模型是一种简单有效的解决亚像元定位问题的方法。

Traditional hard classification of remote sensing images has been proved to be inaccurate due to the presence of mixed pixels in images.Even though the composition of these pixels for different classes can be estimated with pixel unmixing model the output provides no indication of how such classes are distributed spatially within these pixels.Sub-pixel mapping is a technique designed to obtain the spatial distribution of these classes in these pixels with information contained in mixed pixels.A newly Cellular Automata model was proposed to solve the problem of sub-pixel mapping with the assumption of spatial dependence.The model was tested on both synthetic and real images,and the result shows that this Cellular Automata model is a simple and efficient method to solve the sub-pixel mapping problem.