卫星测量的有效统计分类
Efficient statistical classification of satellite measurements
摘要：
监督统计分类是卫星图像处理的重要工具。它不仅在需要特征提取或表面类型等离散结果时有用，而且通过将感兴趣的量划分为离散范围来进行连续检索时也很有用。由于现代卫星仪器的高分辨率和实时处理的要求，任何算法都必须快速才能有用。在这里，我们描述了一种基于核估计的算法，称为自适应高斯滤波，它结合了几个创新，以产生更高的效率相比，其他三个流行的方法：k-最近邻（KNN），学习矢量量化（LVQ）和支持向量机（SVM）。这种效率是在没有任何妥协的情况下获得的：保持准确性，同时返回条件概率的估计值。这些不仅是有用的，以衡量其真实值的情况下，估计的准确性，但也重新校准检索到的图像，并作为一个代理的离散连续变量。该算法进行了演示，并与其他三个对合成测试类和映射荷兰的水道。软件可在http://libagf.sourceforge.net上找到。
英文摘要：
Supervised statistical classification is a vital tool for satellite image processing. It is useful not only when a discrete result, such as feature extraction or surface type, is required, but also for continuum retrievals by dividing the quantity of interest into discrete ranges. Because of the high resolution of modern satellite instruments and because of the requirement for real-time processing, any algorithm has to be fast to be useful. Here we describe an algorithm based on kernel estimation called Adaptive Gaussian Filtering that incorporates several innovations to produce superior efficiency as compared to three other popular methods: k-nearest-neighbour (KNN), Learning Vector Quantization (LVQ) and Support Vector Machines (SVM). This efficiency is gained with no compromises: accuracy is maintained, while estimates of the conditional probabilities are returned. These are useful not only to gauge the accuracy of an estimate in the absence of its true value, but also to re-calibrate a retrieved image and as a proxy for a discretized continuum variable. The algorithm is demonstrated and compared with the other three on a pair of synthetic test classes and to map the waterways of the Netherlands. Software may be found at: http://libagf.sourceforge.net.
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。
