Multiscale Convolutional Mask Network for Hyperspectral Unmixing
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingDeep learning has gained popularity in hyperspectral unmixing (HU) applications recently due to its powerful learning and data-fitting capabilities. As an unmixing baseline network, the autoencoder (AE) framework performs well in HU by automatically ...
Mingming Xu +4 more
doaj +1 more source
Generalized linear mixing model accounting for endmember variability
, 2017 Endmember variability is an important factor for accurately unveiling vital information relating the pure materials and their distribution in hyperspectral images.
Bermudez, José Carlos Moreira +2 more
core +1 more source
Correntropy Maximization via ADMM - Application to Robust Hyperspectral Unmixing
, 2016 In hyperspectral images, some spectral bands suffer from low signal-to-noise ratio due to noisy acquisition and atmospheric effects, thus requiring robust techniques for the unmixing problem.
Chen, Badong +4 more
core +2 more sources
, 2015 In blind hyperspectral unmixing (HU), the pure-pixel assumption is well-known to be powerful in enabling simple and effective blind HU solutions. However, the pure-pixel assumption is not always satisfied in an exact sense, especially for scenarios where
Ambikapathi, ArulMurugan +4 more
core +1 more source
Nonlinear unmixing of vegetated areas: a model comparison based on simulated and real hyperspectral data [PDF]
, 2014 International audienceWhen analyzing remote sensing hyperspectral images, numerous works dealing with spectral unmixing assume the pixels result from linear combinations of the endmember signatures.
Altmann, Yoann +4 more
core +2 more sources
Hyperspectral Imaging: The Intelligent Eye to Uncover the Password of Plant Science
Modern Agriculture, Volume 3, Issue 2, December 2025.Hyperspectral imaging (HSI) has emerged as a powerful non‐destructive technique for characterisation of the plant phenotype and physiological traits. The ongoing development of cost‐effective hardware, coupled with standardised acquisition protocols and open‐access spectral libraries, is accelerating its integration with multi‐omics approaches to ...
Jingyan Song +17 more
wiley +1 more source
Dynamical spectral unmixing of multitemporal hyperspectral images
, 2015 In this paper, we consider the problem of unmixing a time series of hyperspectral images. We propose a dynamical model based on linear mixing processes at each time instant.
Chanussot, Jocelyn +2 more
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A Comparison of Nonlinear Mixing Models for Vegetated Areas Using Simulated and Real Hyperspectral Data [PDF]
, 2014 International audienceAbstract--Spectral unmixing (SU) is a crucial processing step when analyzing hyperspectral data. In such analysis, most of the work in the literature relies on the widely acknowledged linear mixing model to describe the observed ...
Altmann, Yoann +4 more
core +4 more sources
Spectral-spatial adversarial network for nonlinear hyperspectral unmixing of imbalanced datasets
International Journal of Applied Earth Observations and Geoinformation, 2023 With its successful application in various fields, hyperspectral unmixing (HU) technology has received extensive attention in remote sensing processing. Recently, various autoencoders based on the linear mixing model (LMM) have been proposed to provide a
Xu Yang, Jianguo Chen, Zihao Chen
doaj +1 more source
GETNET: A General End-to-end Two-dimensional CNN Framework for Hyperspectral Image Change Detection
, 2019 Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data ...
Du, Qian +9 more
core +1 more source