遥感影像分类后处理的地统计方法

Geostatistical Approaches to Post-classification of Remote Sensing Image

采用地统计学中带局部均值的简单克里格方法和协同克里格方法,利用基于训练样本的指示数据(位置和类别)和基于光谱初分类的类别概率数据的空间结构信息,对未知点位的类别发生概率进行预测,从而修正初分类结果。实验结果表明,两种方法所获得的精度相较初分类的精度均有明显提高,这种充分利用训练样本信息改善分类结果的策略不局限于特定的初始分类器。

This paper explores two methods pertaining to geostatistics,i.e.,simple kriging with local mean and cokriging,to predict class occurrences based on training samples＇ indicator transforms（location and classes） and spectrally derived class probabilities,thus calibrating the a posterior class probability vectors derived from initial spectral classification.The results showed that classification accuracy is significantly increased by these two methods for utilizing spatial information contained in training samples and initial spectral classification,compared with those obtainable with spectral classification.Moreover,the proposed methods constitute a valuable strategy for making fuller use of information residing in training data for improving spectrally derived classification,which is independent of the specific classifiers initially adopted for image classification.