Hyperspectral Imaging and Applications

Hyperspectral Imaging and Applications

Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications"...

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HerausgeberIn:
Song, Meiping
Zhang, Junping
Wu, Chao-Cheng
Sonstige:
Chang, Chein-I
Song, Meiping
Zhang, Junping
Wu, Chao-Cheng
Year of Publication:2022
Language:English
Physical Description:1 electronic resource (632 p.)
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ctrlnum (CKB)5600000000483066
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collection bib_alma
record_format marc
spelling Chang, Chein-I edt
Hyperspectral Imaging and Applications
Basel MDPI - Multidisciplinary Digital Publishing Institute 2022
1 electronic resource (632 p.)
text txt rdacontent
computer c rdamedia
online resource cr rdacarrier
Due to advent of sensor technology, hyperspectral imaging has become an emerging technology in remote sensing. Many problems, which cannot be resolved by multispectral imaging, can now be solved by hyperspectral imaging. The aim of this Special Issue "Hyperspectral Imaging and Applications" is to publish new ideas and technologies to facilitate the utility of hyperspectral imaging in data exploitation and to further explore its potential in different applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis. 1. Data Unmixing (2 papers)2. Spectral variability (2 papers)3. Target Detection (3 papers)4. Hyperspectral Image Classification (6 papers)5. Band Selection (2 papers)6. Data Fusion (2 papers)7. Applications (8 papers) Under every category each paper is briefly summarized by a short description so that readers can quickly grab its content to find what they are interested in.
English
Technology: general issues bicssc
History of engineering & technology bicssc
biodiversity
peatland
vegetation type
classification
hyperspectral
in situ measurements
hyperspectral image (HSI)
multiscale union regions adaptive sparse representation (MURASR)
multiscale spatial information
imaging spectroscopy
airborne laser scanning
minimum noise fraction
class imbalance
Africa
agroforestry
tree species
hyperspectral unmixing
endmember extraction
band selection
spectral variability
prototype space
ensemble learning
rotation forest
semi-supervised local discriminant analysis
optical spectral region
thermal infrared spectral region
mineral mapping
data integration
HyMap
AHS
raw material
remote sensing
nonnegative matrix factorization
data-guided constraints
sparseness
evenness
hashing ensemble
hierarchical feature
hyperspectral classification
band expansion process (BEP)
constrained energy minimization (CEM)
correlation band expansion process (CBEP)
iterative CEM (ICEM)
nonlinear band expansion (NBE)
Otsu’s method
sparse unmixing
local abundance
nuclear norm
hyperspectral detection
target detection
sprout detection
constrained energy minimization
iterative algorithm
adaptive window
hyperspectral imagery
recursive anomaly detection
local summation RX detector (LS-RXD)
sliding window
band selection (BS)
band subset selection (BSS)
hyperspectral image classification
linearly constrained minimum variance (LCMV)
successive LCMV-BSS (SC LCMV-BSS)
sequential LCMV-BSS (SQ LCMV-BSS)
vicarious calibration
reflectance-based method
irradiance-based method
Dunhuang site
90° yaw imaging
terrestrial hyperspectral imaging
vineyard
water stress
machine learning
tree-based ensemble
progressive sample processing (PSP)
real-time processing
image fusion
hyperspectral image
panchromatic image
structure tensor
image enhancement
weighted fusion
spectral mixture analysis
fire severity
AVIRIS
deep belief networks
deep learning
texture feature enhancement
band grouping
hyperspectral compression
lossy compression
on-board compression
orthogonal projections
Gram–Schmidt orthogonalization
parallel processing
anomaly detection
sparse coding
KSVD
hyperspectral images (HSIs)
SVM
composite kernel
algebraic multigrid methods
hyperspectral pansharpening
panchromatic
intrinsic image decomposition
weighted least squares filter
spectral-spatial classification
label propagation
superpixel
semi-supervised learning
rolling guidance filtering (RGF)
graph
deep pipelined background statistics
high-level synthesis
data fusion
data unmixing
hyperspectral imaging
3-03921-522-1
3-03921-523-X
Song, Meiping edt
Zhang, Junping edt
Wu, Chao-Cheng edt
Chang, Chein-I oth
Song, Meiping oth
Zhang, Junping oth
Wu, Chao-Cheng oth
language English
format eBook
author2 Song, Meiping
Zhang, Junping
Wu, Chao-Cheng
Chang, Chein-I
Song, Meiping
Zhang, Junping
Wu, Chao-Cheng
author_facet Song, Meiping
Zhang, Junping
Wu, Chao-Cheng
Chang, Chein-I
Song, Meiping
Zhang, Junping
Wu, Chao-Cheng
author2_variant c i c cic
m s ms
j z jz
c c w ccw
author2_role HerausgeberIn
HerausgeberIn
HerausgeberIn
Sonstige
Sonstige
Sonstige
Sonstige
title Hyperspectral Imaging and Applications
spellingShingle Hyperspectral Imaging and Applications
title_full Hyperspectral Imaging and Applications
title_fullStr Hyperspectral Imaging and Applications
title_full_unstemmed Hyperspectral Imaging and Applications
title_auth Hyperspectral Imaging and Applications
title_new Hyperspectral Imaging and Applications
title_sort hyperspectral imaging and applications
publisher MDPI - Multidisciplinary Digital Publishing Institute
publishDate 2022
physical 1 electronic resource (632 p.)
isbn 3-03921-522-1
3-03921-523-X
illustrated Not Illustrated
work_keys_str_mv AT changcheini hyperspectralimagingandapplications
AT songmeiping hyperspectralimagingandapplications
AT zhangjunping hyperspectralimagingandapplications
AT wuchaocheng hyperspectralimagingandapplications
status_str n
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carrierType_str_mv cr
is_hierarchy_title Hyperspectral Imaging and Applications
author2_original_writing_str_mv noLinkedField
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