IEEE Geoscience and Remote Sensing Magazine - March 2022 - 12

TABLE 1. THE TAXONOMY OF HS, SR, AND LRR METHODS.
CATEGORY
Denoising
SUBCATEGORY TYPICAL EXAMPLES
SR
Low-rank matrix
recovery (LRMR)
Tensor
decomposition
Superresolution Matrix-based
methods
Tensor-based
approaches
Dimension
reduction
Unmixing
Feature
extraction
Band selection
Sparsity
regularizer
LRR
Classification
SR
Joint SR (JSR)
K-SVD [11], ABPFA [12], CHyDU [13], OSDL [6], Spa+Lr [5], MTSNMF [14], JSSDSR [15], SSASR [16], adaptive
spatial-spectral dictionary learning [17], 3D NLS [18], and FastHyDe [19]
RPCA [20], LRMR [3], LRSNL [21], group LRR [22], SNLRSF [23], SSLR [24], NGmeet [25], graph LRR [26],
two-phase matrix decomposition [27], NAILRMA [28], LRTV [29], LLRSSTV [30], LRRSDS [31], TWNNM [32],
WNNTV [33], LSSTV [34], WSN [35], [36], SLRR [37], FS2LRL [38], SS-LRR [39], and GLF [40]
LRTA [41], nonnegative Tucker decomposition [42], LRTDTV [43], GKTD [44], CPTD [45], R1TD [46], NLR-CPTD
[45], LRTR [39], STWNNM [47], SSTV-LRTF [48], GSLRTD [49], and NLTR [50]
LMM for MS/HS fusion [51], HS/MS (RGB) fusion via matrix factorization [52], CNMF-based methods [53]-
[55], local spectral unmixing [56], [57], LRSR [58], LRFF [59], Bayesian nonparametric SR [60], variational SR
[61], HySR-SpaSpeF [62], and local low-rank regularization [63]
CSTF [64], NLSTF_SMBF [65], NCTCP [66], coupled Tucker approximation [67], SSGLRTD [68], LTTR [69],
WLRTR [70], LTMR [71], and NLRTATV [72]
LRSSC [73], [74], LSS [75], KLRSSC [76], LhalfLRR [77], T-LGMR [78], SLGDA [79], KSLGDA [80], TSLGDA [81],
SLRNILE [82], LRR_NP [83], and WLRR [84]
SNMF [85], SNMF-TEMD [86], MTSP [87], ISSC [88], SWLRSC [89], FRSR [90], DWSSR [91], SSR [92], LLRSC [9],
and FLLRSC [10]
,
,
1 -norm regularizer [7], [93], S-measure constraint [94], ,p -norm regularizers [1], [95], [96], , NMF [8],
12 /
reweighted sparse regularizer [97], [98], double weights [99], [100], collaborative sparsity [101], [102],
21, -norm
regularizer [103], group sparsity regularizer [104], [105], ,ro ,0w
20 ,
norm [106], and , norm [107]
Semisupervised sparse unmixing [108], ADSpLRU [109], JSpBLRU [110], HURLR-TV [111], GLrNMF [112],
SCC-LRR [113], J-LASU [114], SUnSAL-TV-LA [115], ALMSpLRU [116], RGBM-SS-LRR [117], SULoRA [118],
cDeUn [119], CHyDU [13], and SNDeUn [120]
SRC [2], [121], [122], SRC on spectral-spatial features [123]-[125], SRC-TS [126], probabilistic SR [127],
cdSRC [128], MSRC [129], SRNN [130], SSSRC [131], SRC-CR [132], S-RBFKLN [133], DWSRC [134], SADL [135],
shapelet-based SR [136], and mlSRC [137]
JSR [2], ASOMP [138], SBDSM [139], SAJSRC [140], NLWJSR [141], NRJSR [142], LAJSR [143], MCCJSR [144],
SPJSR [4], MLEJSR [145], other robust JSR methods [146], [147], JSR with structured sparsity priors [148], JSR
with manifold-based constraint [149], MASR [150], MF-JSRC [151], MFASR [152], SMTJSRC [153], LSGM [154],
SRSTSD [155], DKSVD [84], CCJSR [156], and KJSR [157]-[162]
Anomaly
detection
LRR
Constraints
embedding
LRMD [163], RPCA-RX [163], LRRSTO [164], LRRaLD [165], and SLW_LRRSTO and MLW_LRRSTO [166]
LRASR [167], LRCRD [168], LRaSMD [169], LSMAD [170], LwOaW [171], RSLAD [172], LSDM-MoG [173], GTVLRR
[174], and LTDD [175]
NLS: nonlocal sparse; FastHyDe: fast HS denoising; CHyDU: coupled HSI denoising and unmixing; RPCA: robust principal component analysis; LRCRD: low-rank collaborative representation;
OSDL: online spectral dictionary learning; SNMF: sparse nonnegative matrix factorization; SNMF-TEMD: sparse nonnegative matrix factorization-thresholded ground distance;
MTSNMF: multitask sparse nonnegative matrix factorization; JSSDSR: joint spectral-spatial distributed SR; SSASR: spectral-spatial adaptive SR; LRSNL: low-rank spectral nonlocal;
SNLRSF: subspace-based nonlocal low-rank and sparse factorization; SSLR: spatial-spectral low rank; NAILRMA: noise-adjusted iterative low-rank matrix approximation; LRTV: total
variation regularized low rank; LLRSSTV: spatial-spectral total variation-regularized local low-rank; LRRSDS: low-rank constraint on the spectral difference; TWNNM: total variation-regularized
weighted nuclear norm minimization; WNNTV: weighted nuclear norm and total variation regularization; LSSTV: low-rank constraint and spatial-spectral total variation; WSN:
weighted Schatten p-norm; SLRR: subspace LRR; LRTA: low-rank tensor approximation; LRTDTV: total variation-regularized low-rank tensor decomposition; GKTD: genetic kernel Tucker
decomposition; CPTD: CANDECOMP/PARAFAC tensor decomposition; R1TD: rank-1 tensor decomposition; STWNNM: structure tensor total variation-regularized weighted nuclear norm
minimization; NLR-CPTD: nonlocal low-rank-regularized CANDECOMP/PARAFAC tensor decomposition; LRTR: low-rank tensor recovery; SSTV-LRTF: spatial-spectral total variation-regularized
low-rank tensor factorization; GSLRTD: group sparse and low-rank tensor decomposition; LMM: linear mixing model; RGB: red, green, blue; CNMF: coupled nonnegative matrix
factorization; NLTR: nonlocal tensor ring; CCJSR: correlation coefficient JSR; LRFF: low-rank factorization fusion; LRSSC: low-rank sparse subspace clustering; CSTF: coupled sparse tensor
factorization; LSS: rank sparse subspace; KLRSSC: kernel low-rank sparse subspace clustering; LhalfLRR: 1/2
,
regularization-based LRR; T-LGMR: tensor-based low-rank graph with
multimanifold regularization; NCTCP: nonlocal coupled tensor canonical polyadic; SSGLRTD: spatial-spectral-graph-regularized low-rank tensor decomposition; LTTR: low tensor-train
rank; WLRTR: weighted low-rank tensor recovery; LTMR: low tensor multirank; NLSTF: nonlocal sparse tensor factorization; kernel low-rank sparse subspace clustering; MTSP: multitask
sparsity pursuit; ISSC: improved sparse subspace clustering; SWLRSC: squaring weighted low-rank subspace clustering; FRSR: fast and robust self-representation; DWSSR: dissimilarityweighted
sparse self-representation; SSR: symmetric SR; LLRSC: Laplacian-regularized low-rank subspace clustering; FLLRSC: fast and latent low-rank subspace clustering; SLGDA:
sparse and low-rank graph-based discriminant analysis; KSLGDA: kernel sparse and low-rank graph-based discriminant analysis; TSLGDA: tensor sparse and low-rank graph-based discriminant
analysis; SLRNILE: sparse and low-rank near-isometric linear embedding; LRR_NP: LRR with neighborhood preserving; WLRR: weighted LRR; ADSpLRU: alternating direction
sparse and low-rank unmixing; JSpBLRU: joint sparse blocks and low-rank unmixing; HURLR-TV: HS unmixing by reweighted low-rank and total variation; SCC-LRR: LRR with space-consistency
constraint; GLrNMF: group low-rank, constrained nonnegative matrix factorization; SUnSAL-TV-LA: sparse unmixing via variable splitting augmented Lagrangian and total variation
local abundance; J-LASU: joint local-abundance sparse unmixing; ALMSpLRU: alternating minimization sparse low-rank unmixing; RGBM: robust generalized bilinear; RGBM-SSLRR:
robust generalized bilinear based on a nonlinear unmixing method with SS and LRR; SULoRA: subspace unmixing with low-rank attribute; cDeUn: coupled denoising and unmixing;
SRC: SR-based classification; SRC-TS: SR-based classification in the tangent space; SNDeUn: simultaneous nonconvex denoising and unmixing; cdSRC: class-dependent SRC; MSRC: multiobjective-based
SR-based classification; SRNN: SR-based nearest-neighbor classification; SSSRC: spectral-spatial-combined SR-based classification; S-RBFKLN: sparse radial basis function
kernel learning network; DWSRC: dissimilarity-weighted SR-based classification; SADL: spatial-aware dictionary learning; mlSRC: multilayer SR-based classification; NLWJSR: nonlocal-weighted
JSR; NRJSR: nearest-regularized JSR; MASR: multiscale adaptive SR; MF-JSRC: multiple-feature JSR classification; MFASR: multiple-feature-based adaptive SR; LSGM: local
sparsity graphical model; SRSTSD: SR based on the set-to-set distance; DKSVD: discriminative K-SVD; KJSR: kernel-based JSR; ASOMP: adaptive SOMP: MLEJSR: maximum-likelihood
estimation-based JSR; RPCA-RX: robust principal component analysis receiver; LRMD: low-rank matrix decomposition; LRRSTO: LRR sum-to-one; LRRSTO SLWs_LRRSTO: LRR sum-toone
single local windows; LRRSTO MLWs_LRRSTO: LRR sum-to-one multiple local windows; LRaSMD: low-rank and sparse matrix decomposition; LSMAD: LRaSMD-based Mahalanobis
distance method for H anomaly detection; LwOaW: LRaSMD with orthogonal subspace projection-based background suppression and adaptive weighting; SLAD: randomized subspace
learning-based anomaly detector; GTVLRR: graph and total variation-regularized LRR; LTDD: low-rank tensor decomposition-based anomaly detection; SRC-CR: SR-based classification
collaborative representation; SRSTSD: SR based on the set-to-set distance ; Spa+Lr: sparse representation and low rank; GLF: global local factorization; FS2LRL: fast superpixel-based
subspace low-rank learning; LRSR: low spatial resolution super-resolution; HySR-SpaSpecF: HS super-resolution based on spatial-spectral correlation fusion; NLSTF_SMBF: nonlocal
sparse tensor factorization for the semiblind fusion; NLRTATV: Nonlocal Low-Rank Tensor Approximation and Total Variation; SBDSM: superpixel-based discriminative sparse model;
MCCJSR: maximum correntropy criterion based JSR; SAJSRC: shape-adaptive JSR classification; LAJSR: local adaptation JSR; SPJSR: self-paced JSR; SMTJSRC: superpixel-level multitask
JSR classification; LSDM-MoG: low-rank and sparse decomposition model with mixture of Gaussian.
12
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MARCH 2022
IEEE Geoscience and Remote Sensing Magazine - March 2022

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