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With the increasing acquisition ability of hyperspectral remote sensing images, unsupervised cross-temporal classification (UCTC) of hyperspectral images (HSIs) has attracted more and more attention. In this paper, we focus on cross-temporal HSI classification, i.e., using one labeled HSI to classify the other unlabeled HSI. A multiple geodesic flow kernel learning (MGFKL) framework is proposed to exploit both spatial and spectral features for UCTC with bitemporal HSIs and called S2-MGFKL. The proposed S2-MGFKL method first extracts extended multi-attribute profiles (EMAPs) from the original bitemporal HSIs. The spatial features of the bitemporal HSIs obtained by the same attribute filter are paired up, so are the original spectral features. Second, each pair of features from both source and target domains are used to construct multiple geodesic flows. According to the original definition of GFK, we can obtain the construction of Gaussian base GFKs. The base kernels consist of two parts, the spectral part is obtained base on the same geodesic flow (which is constructed on the bitemporal spectral features) by tuning the kernel scale, while the spatial part is obtained under the same kernel scale but different geodesic flows constructed on different spatial feature pairs. After that, the mean rule is adopted to acquire the combined kernel, which is fed into the supervised vector machine (SVM) to implement the cross-temporal classification task. Experiments are conducted on two real HSI data sets, and the results compared with several well-known methods demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In recent years, many studies on hyperspectral image classification have shown that using multiple features can effectively improve the classification accuracy. As a very powerful means of learning, multiple kernel learning (MKL) can conveniently be embedded in a variety of characteristics. This paper proposes a class-specific sparse MKL (CS-SMKL) framework to improve the capability of hyperspectral image classification. In terms of the features, extended multiattribute profiles are adopted because it can effectively represent the spatial and spectral information of hyperspectral images. CS-SMKL classifies the hyperspectral images, simultaneously learns class-specific significant features, and selects class-specific weights. Using an L1-norm constraint (i.e., group lasso) as the regularizer, we can enforce the sparsity at the group/feature level and automatically learn a compact feature set for the classification of any two classes. More precisely, our CS-SMKL determines the associated weights of optimal base kernels for any two classes and results in improved classification performances. The advantage of the proposed method is that only the features useful for the classification of any two classes can be retained, which leads to greatly enhanced discriminability. Experiments are conducted on three hyperspectral data sets. The experimental results show that the proposed method achieves better performances for hyperspectral image classification compared with several state-of-the-art algorithms, and the results confirm the capability of the method in selecting the useful features. [ABSTRACT FROM PUBLISHER]

In this paper, we propose a novel multiple kernel learning (MKL) framework to incorporate both spectral and spatial features for hyperspectral image classification, which is called multiple-structure-element nonlinear MKL (MultiSE-NMKL). In the proposed framework, multiple structure elements (MultiSEs) are employed to generate extended morphological profiles (EMPs) to present spatial–spectral information. In order to better mine interscale and interstructure similarity among EMPs, a nonlinear MKL (NMKL) is introduced to learn an optimal combined kernel from the predefined linear base kernels. We integrate this NMKL with support vector machines (SVMs) and reduce the min–max problem to a simple minimization problem. The optimal weight for each kernel matrix is then solved by a projection-based gradient descent algorithm. The advantages of using nonlinear combination of base kernels and multiSE-based EMP are that similarity information generated from the nonlinear interaction of different kernels is fully exploited, and the discriminability of the classes of interest is deeply enhanced. Experiments are conducted on three real hyperspectral data sets. The experimental results show that the proposed method achieves better performance for hyperspectral image classification, compared with several state-of-the-art algorithms. The MultiSE EMPs can provide much higher classification accuracy than using a single-SE EMP. [ABSTRACT FROM AUTHOR]