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Abstract
Abstract. In recent years, hyperspectral imaging, also known as imaging spectroscopy, has been paid an increasing interest in geoscience and remote sensing community. Hyperspectral imagery is characterized by very rich spectral information, which enables us to recognize the materials of interest lying on the surface of the Earth more easier. We have to admit, however, that high spectral dimension inevitably brings some drawbacks, such as expensive data storage and transmission, information redundancy, etc. Therefore, to reduce the spectral dimensionality effectively and learn more discriminative spectral low-dimensional embedding, in this paper we propose a novel hyperspectral embedding approach by simultaneously considering spatial and spectral information, called spatial-spectral manifold embedding (SSME). Beyond the pixel-wise spectral embedding approaches, SSME models the spatial and spectral information jointly in a patch-based fashion. SSME not only learns the spectral embedding by using the adjacency matrix obtained by similarity measurement between spectral signatures, but also models the spatial neighbours of a target pixel in hyperspectral scene by sharing the same weights (or edges) in the process of learning embedding. Classification is explored as a potential strategy to quantitatively evaluate the performance of learned embedding representations. Classification is explored as a potential application for quantitatively evaluating the performance of these hyperspectral embedding algorithms. Extensive experiments conducted on the widely-used hyperspectral datasets demonstrate the superiority and effectiveness of the proposed SSME as compared to several state-of-the-art embedding methods.
Highlights
Operational hyperspectral missions, such as DLR Earth Sensing Imaging Spectrometer (DESIS) (Krutz et al, 2018), Gaofen-5 (Ren et al, 2017), Environmental Mapping and Analysis Program (EnMAP) (Guanter et al, 2009), enable the recognition and identification of the materials of interest at a more accurate level compared to the multispectral data (Hong et al, 2015) or RGB data (Wu et al, 2018, Wu et al, 2019)
It is well known that the hyperspectral imagery is a three-dimensional imaging product by continuously scanning the region of interest (ROI) to obtain hundreds or thousands of two-dimensional images finely sampled from the wavelength nearly covering the whole electromagnetic spectrum, e.g., 300nm to 2500nm
Several hyperspectral embedding baselines are selected to evaluate the quality of learned embedding representations using the different methods, such as the original spectral features (OSF), principal component analysis (PCA) (Wold et al, 1987), Laplacian eigenmaps (LE), locally linear embedding (LLE), and ours (SSME)
Summary
Operational hyperspectral missions, such as DLR Earth Sensing Imaging Spectrometer (DESIS) (Krutz et al, 2018), Gaofen-5 (Ren et al, 2017), Environmental Mapping and Analysis Program (EnMAP) (Guanter et al, 2009), enable the recognition and identification of the materials of interest at a more accurate level compared to the multispectral data (Hong et al, 2015) or RGB data (Wu et al, 2018, Wu et al, 2019). It is well known that the hyperspectral imagery is a three-dimensional imaging product by continuously scanning the region of interest (ROI) to obtain hundreds or thousands of two-dimensional images finely sampled from the wavelength nearly covering the whole electromagnetic spectrum, e.g., 300nm to 2500nm This enables the identification and detection of materials lying on the surface of the Earth at a more accurate level compared to other optical data, e.g., RGB. High storage and computational cost, redundant information, and complex noises caused by atmospheric correlation would have a negative influence on the spectral discrimination of hyperspectral images, further degrading the performance of high-level applications, e.g., classification, detection A novel hyperspectral dimensionality reduction approach – spatial-spectral manifold embedding (SSME) – is devised to learn the low-dimensional manifold embedding of the hyperspectral data.
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