Abstract
Hyperspectral images (HSI) possess abundant spectral bands and rich spatial information, which can be utilized to discriminate different types of land cover. However, the high dimensional characteristics of spatial-spectral information commonly cause the Hughes phenomena. Traditional feature learning methods can reduce the dimensionality of HSI data and preserve the useful intrinsic information but they ignore the multi-manifold structure in hyperspectral image. In this paper, a novel dimensionality reduction (DR) method called spatial-spectral multiple manifold discriminant analysis (SSMMDA) was proposed for HSI classification. At first, several subsets are obtained from HSI data according to the prior label information. Then, a spectral-domain intramanifold graph is constructed for each submanifold to preserve the local neighborhood structure, a spatial-domain intramanifold scatter matrix and a spatial-domain intermanifold scatter matrix are constructed for each sub-manifold to characterize the within-manifold compactness and the between-manifold separability, respectively. Finally, a spatial-spectral combined objective function is designed for each submanifold to obtain an optimal projection and the discriminative features on different submanifolds are fused to improve the classification performance of HSI data. SSMMDA can explore spatial-spectral combined information and reveal the intrinsic multi-manifold structure in HSI. Experiments on three public HSI data sets demonstrate that the proposed SSMMDA method can achieve better classification accuracies in comparison with many state-of-the-art methods.
Highlights
Hyperspectral image (HSI) is acquired by different imaging spectrometer sensors (e.g., EO-1 Hyperion, HyMap and AVIRIS), which possesses abundant spectral information about ground objects in hundreds of spectral bands [1,2,3]
Experiments on these HSI data sets demonstrate that the proposed spatial-spectral multiple manifold discriminant analysis (SSMMDA) method can explore spatial-spectral combined information and reveal the intrinsic multi-manifold structure in HSI, which can significantly improve the classification performance of HSI
For the multiple manifold-based dimensionality reduction (DR) algorithms, the proposed SSMMDA method achieves better classification results than supervised multi-manifold learning (SMML) and MMDA in most conditions, this is because SMML and MMDA only consider the spectral information of HSI and they do not utilize the spatial structure to further enhance the classification performance
Summary
Hyperspectral image (HSI) is acquired by different imaging spectrometer sensors (e.g., EO-1 Hyperion, HyMap and AVIRIS), which possesses abundant spectral information about ground objects in hundreds of spectral bands [1,2,3]. Due to the advancement of imaging spectrometer technology, the spectral resolution of hyperspectral sensors has been improved significantly, which can provide richer spectral information to differentiate different ground objects [4,5]. The sensors with higher resolution produce a very large volume of data, it will render the traditional image processing algorithms designed for multispectral imagery ineffective [6,7]. The reason is that the sample point in HSI often exhibits similar characteristics to electromagnetic waves in adjacent bands, which will produce a lot of useless information and restrict the classification performance of HSI. To achieve an excellent classification performance, it is an urgent task to perform dimensionality reduction (DR) for HSI data while preserving the intrinsic valuable information
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