Classification of airborne hyperspectral data based on the average learning subspace method
Classification of airborne hyperspectral data based on the average learning subspace method
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DOI：
10.1109/lgrs.2008.915941
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发表时间：
2008-07-01
影响因子：
4.8
通讯作者：
Feng, Zhaosheng
中科院分区：
文献类型：
--
作者：
Bagan, Hasi;Yasuoka, Yoshifumi;Feng, Zhaosheng
本文介绍了可直接应用于原始高光谱的平均学习子空间方法。用于土地覆盖分类的数据。ALSM分类算法包括以下迭代步骤：1)使用类特征信息压缩方法为训练数据集中的每个类生成初始合适的特征子空间；2)根据最大投影原则更新子空间。我们将ALSM与支持向量机分类器进行了比较。通过在两个高光谱数据集(分别为48个波段和191个波段)上的实验，我们证明了ALSM可以同时进行降维和分类。与支持向量机分类器相比，在某些情况下，ALSM可以达到更高的分类精度。
This letter introduces the averaged learning subspace method (ALSM) that can be applied directly to original hyperspectral. data for the purpose of classifying land cover. The ALSM algorithm of classification consists of the following iterative steps: 1) generate the initial appropriate feature subspace for each class in training datasets using the class-featuring information compression method, and 2) update the subspaces according to the maximum projection principle. We compare ALSM with the support vector machine classifier. By conducting experiments on two hyperspectral datasets (48 bands and 191 bands, respectively), we demonstrate that the ALSM can make dimensional reduction and classification simultaneously. When compared with the SVM classifier, it appears that the ALSM can achieve a higher accuracy on classification in some cases.