Abstract
Abstract. In remote sensing image interpretation, it is important to combine multiple features of a certain pixel in both spatial and spectral domains to improve the classification accuracy, such as spectral signature, morphological property, and shape feature. Therefore, it is essential to consider the complementary property of different features and combine them in order to obtain an accurate classification rate. In this paper, we introduce a multi-feature dimension reduction algorithm under a probabilistic framework, modified stochastic neighbor embedding (MSNE). For each feature, a probability distribution is constructed based on SNE, and then we alternatively solve SNE and learn the optimal combination coefficients for different features in optimization. Compared with conventional dimension reduction strategies, the suggested algorithm can considers spectral, morphological and shape features of a pixel to achieve a physically meaningful low-dimensional feature representation by automatically learn a combination coefficient for each feature adapted to its contribution to subsequent classification. In experimental section, classification results using hyperspectral remote sensing image (HSI) show that this modified stochastic neighbor embedding can effectively improve classification performance.
Highlights
In hyperspectral remote sensing image (HSI) classification, it is important to employ multiple features of different types to represent a pixel’s information, such as spectral signature (Plaza, Benediktsson et al 2009), morphological property (Soille and Pesaresi 2002), and shape feature (Segl, Roessner et al 2003)
We introduce a multi-feature dimension reduction algorithm under a probabilistic framework which could considered the spectral, Differential Morphological Profiles (DMPs) and shape features of a pixel to achieve a physically meaningful low dimensional representation for an effective and accurate classification
A probability distribution is constructed based on stochastic neighbor embedding (SNE), and we alternatively solve SNE and learn the optimal combination coefficients for different features in optimization
Summary
In hyperspectral remote sensing image (HSI) classification, it is important to employ multiple features of different types to represent a pixel’s information, such as spectral signature (Plaza, Benediktsson et al 2009), morphological property (Soille and Pesaresi 2002), and shape feature (Segl, Roessner et al 2003). A conventional approach is concatenating different features into a long vector and applying a particular dimension reduction technique, such as Principal Component Analysis (PCA) (Jolliffe 2002), Fisher Discriminant Analysis (FDA) (Mika, Ratsch et al 1999), Locally Linear Embedding (LLE) (Roweis and Saul 2000), Laplacian Eigenmaps (LE) (Belkin and Niyogi 2003), and so on This direct feature concatenation strategy intrinsically assumes that different features are distributed in a unified feature space, they are not, because they have different physical meanings and statistical properties (Xie, Mu et al 2011).
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