Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles
Spectral and Spatial Classification of Hyperspectral Data Using SVMs and Morphological Profiles
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DOI：
10.1109/tgrs.2008.922034
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发表时间：
2008-11-01
影响因子：
8.2
通讯作者：
Sveinsson, Johannes R.
中科院分区：
文献类型：
--
作者：
Fauvel, Mathieu;Benediktsson, Jon Atli;Sveinsson, Johannes R.
提出了一种高空间分辨率的城市高光谱数据分类方法。该方法是对以往方法的扩展，同时利用空间信息和光谱信息进行分类。以前的一种方法是基于使用来自高光谱数据的几个主成分(PC)并建立几个形态轮廓(MP)。这种方法的缺点是，它主要是为城市结构分类而设计的，它没有充分利用数据中的光谱信息。类似地，通常使用的高光谱数据的像素化分类仅基于光谱内容，缺乏关于图像中特征的结构的信息。该方法克服了这些问题，基于形态信息和原始高光谱数据的融合，即将两个属性向量连接成一个特征向量。在降维后，使用支持向量机分类器进行最终分类。该方法在城市ROSIS数据上进行了实验测试。与仅基于PC的MPS的使用和传统的光谱分类相比，在精确度方面取得了显著的改进。例如，对于一个数据集，在没有任何特征约简的情况下，总体准确率从79%提高到83%，而在特征约简的情况下，总体准确率提高到87%。所提出的方法在有限的训练集上也显示了很好的效果。
A method is proposed for the classification of urban hyperspectral data with high spatial resolution. The approach is an extension of previous approaches and uses both the spatial and spectral information for classification. One previous approach is based on using several principal components (PCs) from the hyperspectral data and building several morphological profiles (MPs). These profiles can be used all together in one extended MP A shortcoming of that approach is that it was primarily designed for classification of urban structures and it does not fully utilize the spectral information in the data. Similarly, the commonly used pixelwise classification of hyperspectral data is solely based on the spectral content and lacks information on the structure of the features in the image. The proposed method overcomes these problems and is based on the fusion of the morphological information and the original hyperspectral data, i.e., the two vectors of attributes are concatenated into one feature vector. After a reduction of the dimensionality, the final classification is achieved by using a support vector machine classifier. The proposed approach is tested in experiments on ROSIS data from urban areas. Significant improvements are achieved in terms of accuracies when compared to results obtained for approaches based on the use of MPs based on PCs only and conventional spectral classification. For instance, with one data set, the overall accuracy is increased from 79% to 83% without any feature reduction and to 87% with feature reduction. The proposed approach also shows excellent results with a limited training set.