Adaptive Markov random fields for joint unmixing and segmentation of hyperspectral image [PDF]
, 2013 Linear spectral unmixing is a challenging problem in hyperspectral imaging that consists of decomposing an observed pixel into a linear combination of pure spectra (or endmembers) with their corresponding proportions (or abundances). Endmember extraction
Benediktsson, Jon Atli +3 more
core +3 more sources
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 Band redundancy and limitation of labeled samples restrict the development of hyperspectral image classification (HSIC) greatly. To address the earlier issues, the classification models such as subspace-based support vector machines, which have gained a ...
Jiaochan Hu +5 more
doaj +1 more source
A low-cost hyperspectral scanner for natural imaging and the study of animal colour vision above and under water [PDF]
, 2019 Hyperspectral imaging is a widely used technology for industrial and scientific purposes, but the high cost and large size of commercial setups have made them impractical for most basic research.
Baden, T, Nevala, N E
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Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture
Remote Sensing, 2020 Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming.
B. Lu +4 more
semanticscholar +1 more source
Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image [PDF]
, 2017 As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because
Cao, Faxian +4 more
core +3 more sources
Band Subset Selection for Hyperspectral Image Classification
Remote Sensing, 2018 This paper develops a new approach to band subset selection (BSS) for hyperspectral image classification (HSIC) which selects multiple bands simultaneously as a band subset, referred to as simultaneous multiple band selection (SMMBS), rather than one ...
Chunyan Yu, Meiping Song, Chein-I Chang
doaj +1 more source
A New GPU Implementation of Support Vector Machines for Fast Hyperspectral Image Classification
Remote Sensing, 2020 The storage and processing of remotely sensed hyperspectral images (HSIs) is facing important challenges due to the computational requirements involved in the analysis of these images, characterized by continuous and narrow spectral channels.
Mercedes E. Paoletti +4 more
doaj +1 more source
Compressive Hyperspectral Imaging Using Progressive Total Variation [PDF]
, 2014 Compressed Sensing (CS) is suitable for remote acquisition of hyperspectral images for earth observation, since it could exploit the strong spatial and spectral correlations, llowing to simplify the architecture of the onboard sensors. Solutions proposed
Barducci, Alessandro +4 more
core +2 more sources
Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery [PDF]
, 2009 This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing ...
Alfred O. Hero +5 more
core +9 more sources
Deep Learning for Classification of Hyperspectral Data: A Comparative Review [PDF]
IEEE Geoscience and Remote Sensing Magazine, 2019 In recent years, deep-learning techniques revolutionized the way remote sensing data are processed. The classification of hyperspectral data is no exception to the rule, but it has intrinsic specificities that make the application of deep learning less ...
N. Audebert +2 more
semanticscholar +1 more source