Abstract
The fusion of spatial and spectral information in hyperspectral images (HSIs) is useful for improving the classification accuracy. However, this approach usually results in features of higher dimension and the curse of the dimensionality problem may arise resulting from the small ratio between the number of training samples and the dimensionality of features. To ease this problem, we propose a novel algorithm for spatial-spectral feature extraction based on hypergraph embedding. Firstly, each HSI pixel is regarded as a vertex and the joint of extended morphological profiles (EMP) and spectral features is adopted as the feature associated with the vertex. A hypergraph is then constructed by the K-Nearest-Neighbor method, in which each pixel and its most K relevant pixels are linked as one hyperedge to represent the complex relationships between HSI pixels. Secondly, the hypergraph embedding model is designed to learn a low dimensional feature with the reservation of geometric structure of HSI. An adaptive hyperedge weight estimation scheme is also introduced to preserve the prominent hyperedges by the regularization constraint on the weight. Finally, the learned low-dimensional features are fed to the support vector machine (SVM) for classification. The experimental results on three benchmark hyperspectral databases are presented. They highlight the importance of spatial–spectral joint features embedding for the accurate classification of HSI data. The weight estimation is better for further improving the classification accuracy. These experimental results verify the proposed method.
Highlights
Hyperspectral imaging is an important mode of remote sensing imaging, which has been widely used in a diverse range of applications, including environment monitoring, urban planning, precision agriculture, geological exploration, etc. [1,2,3]
According to the experimental results, our proposed method achieves the highest overall accuracy (OA), AA, and κ among all of the competing methods, which shows the effectiveness of our feature extraction algorithm
We propose a novel algorithm for spatial-spectral feature extraction based on hypergraph learning
Summary
Hyperspectral imaging is an important mode of remote sensing imaging, which has been widely used in a diverse range of applications, including environment monitoring, urban planning, precision agriculture, geological exploration, etc. [1,2,3]. [1,2,3] Most of these applications depend on the key problem of classifying the image pixels within hyperspectral imagery (HSI) into multiple categories, i.e., HSI classification, and extensive research efforts have been focused on this problem [4,5,6,7,8,9]. In HSI, each pixel contains hundreds of spectral bands from the visible to the infrared range of the electromagnetic spectrum. The spectral signature of each pixel can be directly used as the feature for classification. It is well accepted that the HSI pixels within a small spatial neighborhood are often made up of the same materials. Spatial contextual information is useful for classification [10,11]
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