Exploitation of both spectral and spatial information in hyperspectral imagery is important for effective classification. Considering the cubical arrangement of data, the three-dimensional (3D) techniques could be effectively used to model hypespectral features. In this study, the 3D geometric moments are used to extract the rotation, scale, and translation invariant features. Unlike 2D moments, the 3D moments characterise the joint spectral–spatial properties. A classification method is proposed that uses the features derived from 3D geometric moments without vectorising or changing the original structure of the raw hyperspectral image. Unlike many other methods, the new method does not need a separate step for spectral feature extraction or dimensionality reduction. The moments are computed on the raw image that generate a comprehensive and smaller feature set. The experimental results from five benchmark airborne hyperspectral images demonstrate that the 3D moment based method yields good classification results better or comparable to several state-of-the-art methods.

中文翻译：

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使用三维几何矩的高光谱图像分类

利用高光谱图像中的光谱和空间信息对于有效分类非常重要。考虑到数据的立方排列，可以有效地使用三维（3D）技术对超光谱特征进行建模。在这项研究中，使用3D几何矩来提取旋转，缩放和平移不变特征。与2D矩不同，3D矩是联合频谱空间特性的特征。提出了一种分类方法，该方法使用从3D几何矩导出的特征，而无需向量化或更改原始高光谱图像的原始结构。与许多其他方法不同，新方法不需要单独的步骤即可进行光谱特征提取或降维。在原始图像上计算矩，从而生成全面且较小的特征集。来自五个基准机载高光谱图像的实验结果表明，基于3D矩的方法产生的分类效果更好，甚至可以与几种最新方法相媲美。