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The hyperspectral feature extraction technique is one of the most popular topics in the remote sensing community. However, most hyperspectral feature extraction methods are based on region-based local information descriptors while neglecting the correlation and dependencies of different homogeneous regions. To alleviate this issue, this paper proposes a multi-view structural feature extraction method to furnish a complete characterization for spectral–spatial structures of different objects, which mainly is made up of the following key steps. First, the spectral number of the original image is reduced with the minimum noise fraction (MNF) method, and a relative total variation is exploited to extract the local structural feature from the dimension reduced data. Then, with the help of a superpixel segmentation technique, the nonlocal structural features from intra-view and inter-view are constructed by considering the intra- and inter-similarities of superpixels. Finally, the local and nonlocal structural features are merged together to form the final image features for classification. Experiments on several real hyperspectral datasets indicate that the proposed method outperforms other state-of-the-art classification methods in terms of visual performance and objective results, especially when the number of training set is limited. [ABSTRACT FROM AUTHOR]

Multi-sensor data on the same area provide complementary information, which is helpful for improving the discrimination capability of classifiers. In this work, a novel multilevel structure extraction method is proposed to fuse multi-sensor data. This method is comprised of three steps: First, multilevel structure extraction is constructed by cascading morphological profiles and structure features, and is utilized to extract spatial information from multiple original images. Then, a low-rank model is adopted to integrate the extracted spatial information. Finally, a spectral classifier is employed to calculate class probabilities, and a maximum posteriori estimation model is used to decide the final labels. Experiments tested on three datasets including rural and urban scenes validate that the proposed approach can produce promising performance with regard to both subjective and objective qualities. [ABSTRACT FROM AUTHOR]

Combining both spectral and spatial information with enhanced resolution provides not only elaborated qualitative information on surfacing mineralogy but also mineral interactions of abundance, mixture, and structure. This enhancement in the resolutions helps geomineralogic features such as small intrusions and mineralization become detectable. In this paper, we investigate the potential of the resolution enhancement of hyperspectral images (HSIs) with the guidance of RGB images for mineral mapping. In more detail, a novel resolution enhancement method is proposed based on component decomposition. Inspired by the principle of the intrinsic image decomposition (IID) model, the HSI is viewed as the combination of a reflectance component and an illumination component. Based on this idea, the proposed method is comprised of several steps. First, the RGB image is transformed into the luminance component, blue-difference and red-difference chroma components (YCbCr), and the luminance channel is considered as the illumination component of the HSI with an ideal high spatial resolution. Then, the reflectance component of the ideal HSI is estimated with the downsampled HSI image and the downsampled luminance channel. Finally, the HSI with high resolution can be reconstructed by utilizing the obtained illumination and the reflectance components. Experimental results verify that the fused results can successfully achieve mineral mapping, producing better results qualitatively and quantitatively over single sensor data. [ABSTRACT FROM AUTHOR]