Department of Technology of Computers and Communications, University of Extremadura / Escuela Politécnica de Cáceres, Avenida de la Universidad s/n, E-10071 Cáceres, Spain; E-Mails:
Sensors (Basel). 2009;9(1):196-218. doi: 10.3390/s90100196. Epub 2009 Jan 8.
Hyperspectral imaging is a new remote sensing technique that generates hundreds of images, corresponding to different wavelength channels, for the same area on the surface of the Earth. Supervised classification of hyperspectral image data sets is a challenging problem due to the limited availability of training samples (which are very difficult and costly to obtain in practice) and the extremely high dimensionality of the data. In this paper, we explore the use of multi-channel morphological profiles for feature extraction prior to classification of remotely sensed hyperspectral data sets using support vector machines (SVMs). In order to introduce multi-channel morphological transformations, which rely on ordering of pixel vectors in multidimensional space, several vector ordering strategies are investigated. A reduced implementation which builds the multi-channel morphological profile based on the first components resulting from a dimensional reduction transformation applied to the input data is also proposed. Our experimental results, conducted using three representative hyperspectral data sets collected by NASA's Airborne Visible-Infrared Imaging Spectrometer (AVIRIS) sensor and the German Digital Airborne Imaging Spectrometer (DAIS 7915), reveal that multi-channel morphological profiles can improve single-channel morphological profiles in the task of extracting relevant features for classification of hyperspectral data using small training sets.
高光谱成像是一种新的遥感技术，它为地球表面同一区域生成数百张对应不同波长通道的图像。由于训练样本的可用性有限（在实践中非常困难且昂贵）以及数据的极高维度，高光谱图像数据集的监督分类是一个具有挑战性的问题。在本文中，我们探索了在使用支持向量机（SVM）对遥感高光谱数据集进行分类之前，使用多通道形态学轮廓进行特征提取的方法。为了引入多通道形态学变换，该变换依赖于多维空间中像素向量的排序，我们研究了几种向量排序策略。还提出了一种简化的实现方法，该方法基于应用于输入数据的降维变换产生的第一分量构建多通道形态学轮廓。我们使用美国宇航局（NASA）机载可见-红外成像光谱仪（AVIRIS）传感器和德国数字航空成像光谱仪（DAIS 7915）采集的三个具有代表性的高光谱数据集进行的实验结果表明，多通道形态学轮廓可以改进单通道形态学轮廓，从而在使用小训练集对高光谱数据进行分类时提取相关特征。
