Results 21 to 30 of about 77,969 (223)
Hyperspectral Unmixing Using Transformer Network
IEEE Transactions on Geoscience and Remote Sensing, 2022 Currently, this paper is under review in IEEE. Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With their superior performance, transformers have found their way in the field of hyperspectral image classification and achieved promising results. In this article, we harness
Ghosh, Preetam +8 more
openaire +5 more sources
Supervised nonlinear spectral unmixing using a post-nonlinear mixing model for hyperspectral imagery [PDF]
, 2011 This paper presents a nonlinear mixing model for hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are nonlinear functions of pure spectral components contaminated by an additive white Gaussian noise.
Altmann, Yoann +9 more
core +1 more source
Benchmark for Hyperspectral Unmixing Algorithm Evaluation
Informatica, 2023 Over the past decades, many methods have been proposed to solve the linear or nonlinear mixing of spectra inside the hyperspectral data. Due to a relatively low spatial resolution of hyperspectral imaging, each image pixel may contain spectra from multiple materials. In turn, hyperspectral unmixing is finding these materials and their abundances. A few
Vytautas Paura +1 more
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches [PDF]
, 2012 Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras.
Paul Gader +13 more
core +1 more source
DLR HySU—A Benchmark Dataset for Spectral Unmixing
Remote Sensing, 2021 Spectral unmixing represents both an application per se and a pre-processing step for several applications involving data acquired by imaging spectrometers.
Daniele Cerra +10 more
doaj +1 more source
Nonlinear unmixing of hyperspectral images: Models and algorithms [PDF]
, 2013 When considering the problem of unmixing hyperspectral images, most of the literature in the geoscience and image processing areas relies on the widely used linear mixing model (LMM).
McLaughlin, Stephen; id_orcid +14 more
core +1 more source
SNMF-Net: Learning a Deep Alternating Neural Network for Hyperspectral Unmixing [PDF]
, 2021 Hyperspectral unmixing is recognized as an important tool to learn the constituent materials and corresponding distribution in a scene. The physical spectral mixture model is always important to tackle this problem because of its highly ill-posed nature.
Xiong, F +9 more
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Remote Sensing, 2020 This paper proposes a framework for unmixing of hyperspectral data that is based on utilizing the scattering transform to extract deep features that are then used within a neural network.
Yiliang Zeng +3 more
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A Supervised Method for Nonlinear Hyperspectral Unmixing [PDF]
Remote Sensing, 2019 Due to the complex interaction of light with the Earth’s surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occurring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data.
Bikram Koirala +5 more
openaire +4 more sources
Nonlinear spectral unmixing of hyperspectral images using Gaussian processes [PDF]
, 2012 This paper presents an unsupervised algorithm for nonlinear unmixing of hyperspectral images. The proposed model assumes that the pixel reflectances result from a nonlinear function of the abundance vectors associated with the pure spectral components ...
Altmann, Yoann +5 more
core +1 more source