融合光谱-空间信息的高光谱遥感影像增量分类算法

提出了一种融合光谱和空间结构信息的高光谱遥感影像增量分类算法INC_SPEC_MPext。通过主成分分析(PCA)提取高光谱影像的若干主成分,利用数学形态学提取各主分量影像对应的形态学剖面(MP),再将所有主分量影像的形态学剖面归并联结,组成扩展的形态学剖面(MPext)。将MPext与光谱信息相结合以增加知识,最大限度地挖掘未标记样本的有用信息,优化分类器的学习能力。不断从分类器对未标记样本的预测结果中甄选置信度高的样本加入训练集,并迭代地利用扩大的训练集进行分类器构建和样本预测。以不同地表覆盖类型的AVIRIS Indian Pines和Hyperion EO-1Botswana作为测试数据,分别与基于光谱、MPext、光谱和MPext融合的分类方法进行比对。试验结果表明,在训练样本数量有限情况下,INC_SPEC_MPext算法在降低分类成本的同时,分类精度和Kappa系数都有不同程度的提高。

Incremental Classification Algorithm of Hyperspectral Remote Sensing Images Based on Spectral-spatial Information

An incremental classification algorithm INC_SPEC_MPext was proposed for hyperspectral remote sensing images based on spectral and spatial information.The spatial information was extracted by building morphological profiles based on several principle components of hyperspectral image.The morpho-logical profiles were combined together in extended morphological profiles (MPext).Combine spectral and MPext to enrich knowledge and utilize the useful information of unlabeled data at the most extent to optimize the classifier.Pick out high confidence data and add to training set,then retrain the classifier with augmented training set to predict the rest samples.The process was performed iteratively.The proposed algorithm was tested on AVIRIS Indian Pines and Hyperion EO-1 Botswana data,which take on different covers,and experimental results show low classification cost and significant improvements in terms of accuracies and Kappa coefficient under limited training samples compared with the classification results based on spectral,MPext and the combination of sepctral and MPext.