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With the development of observation technology in remote sensing (RS), large-area multispectral (MS) images can be easily obtained. However, due to the limitation of imaging devices, only a limited range of hyperspectral (HS) images with higher spectral resolution can be obtained. This article mainly focuses on how to use limited HS images to improve the classification performance of MS images. In order to solve this problem, this article proposes an MS–HS image collaborative classification method, which integrates coupled dictionary learning and distance preserved probability distribution. First, image reconstruction based on coupled dictionary learning is performed, in which sparse representation and dictionary learning are used to generate HS images from MS images through spectral superresolution, so that the spectral features of the MS data and HS data are converted to the same feature space for feature space alignment. Second, the probability distribution is adapted, in which the marginal and conditional probabilities are adapted to further narrow the difference between the real HS data and the generated HS data. At the same time, the consistency of the data structure of the source domain before and after the mapping is maintained, so that the same class of data is more compact after the mapping and reduces the spacing within the same class. Compared with the state-of-the-art methods, this article conducts the experiments on three MS–HS RS datasets, which demonstrate the superiority of the proposed method. [ABSTRACT FROM AUTHOR]

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]

The combination of spectral and 3-D elevation information provided by hyperspectral images (HSIs) and Light Detection and Ranging (LiDAR) has gained increased attention in the remote sensing field and enabled numerous applications. While various methods have been proposed to fuse these two data streams in pixel, feature, or decision level, a deeper view into the intrinsic relation of surface geometry, material reflectance, and environment illumination is still lacking. In this article, we present a novel supervoxel-based joint intrinsic decomposition framework for HSIs and LiDAR. First, we proposed a novel intrinsic scene model for HSIs and LiDAR point cloud, which tells how we can map LiDAR point cloud into HSI pixels with point-cloud-level normals, reflectance, and incident light direction. Then, we extract supervoxels from the LiDAR point cloud using a graph-based supervoxel method. Finally, we formulate the intrinsic decomposition problem within a supervoxel-based framework which can be optimized effectively and efficiently. The outputs of the proposed model are intrinsic scene properties like incident light direction and point-cloud-level hyperspectral reflectance, with which we can then generate intrinsic hyperspectral point cloud (IHSPC) where each point possesses not only 3-D coordinates and normals but also the reflectance over each wavelength. The performance of our approach is demonstrated with both synthetic and real data. [ABSTRACT FROM AUTHOR]