Optimization of an Instance-Based GOES Cloud Classification Algorithm
Optimization of an Instance-Based GOES Cloud Classification Algorithm
复制标题
基于实例的GOES云分类算法优化
DOI：
10.1175/jam2451.1
复制
发表时间：
2007
影响因子：
3
通讯作者：
R. Wade
中科院分区：
文献类型：
--
作者：
R. Bankert;R. Wade
摘要针对地球静止轨道运行的环境卫星(GOES)云分类器，提出了一种基于实例的最近邻算法。专家标记的样本用作各种围棋图像分类场景的训练集。使用完整的可用训练样本集的分类器的初始实现已被证明是用于实时图像分类的低效方法，需要较长的计算运行时间和大量的计算机资源。研究了各种训练集缩减方法，以找到更小的训练集，这些训练集提供了更快的分类器运行时间，同时最大限度地降低了分类器测试集的精度。还分析了由于使用各种缩减方法而导致的实时图像分类中的一般差异。快速凝聚最近邻(FCNN)方法将单个训练集的规模减少了68.3%(四倍交叉验证测试平均值)，而测试的平均总体准确率为S.
Abstract An instance-based nearest-neighbor algorithm was developed for a Geostationary Operational Environmental Satellite (GOES) cloud classifier. Expert-labeled samples serve as the training sets for the various GOES image classification scenes. The initial implementation of the classifier using the complete set of available training samples has proven to be an inefficient method for real-time image classifications, requiring long computational run times and significant computer resources. A variety of training-set reduction methods were examined to find smaller training sets that provide quicker classifier run times with minimal reduction in classifier testing set accuracy. General differences within real-time image classifications as a result of using the various reduction methods were also analyzed. The fast condensed nearest-neighbor (FCNN) method reduced the size of the individual training sets by 68.3% (fourfold cross-validation testing average) while the average overall accuracy of the testing s...