Merickel, M.B.[Michael B.],
Lundgren, J.C.[James C.],
Shen, S.S.[Sylvia S.],
A Spatial Processing Algorithm to Reduce the Effects of
Mixed Pixels and Increase the Separability Between Classes,
PR(17), No. 5, 1984, pp. 525-533.
Elsevier DOI
BibRef
8400
Foody, G.M.,
Arora, M.K.,
Incorporating Mixed Pixels in the Training, Allocation and
Testing Stages of Supervised Classifications,
PRL(17), No. 13, November 25 1996, pp. 1389-1398.
9701
BibRef
Foody, G.M.[Giles M.],
Pal, M.[Mahesh],
Rocchini, D.[Duccio],
Garzon-Lopez, C.X.[Carol X.],
Bastin, L.[Lucy],
The Sensitivity of Mapping Methods to Reference Data Quality:
Training Supervised Image Classifications with Imperfect Reference
Data,
IJGI(5), No. 11, 2016, pp. 199.
DOI Link
1612
BibRef
Townshend, J.R.G.,
Huang, C.,
Kalluri, S.N.V.,
Defries, R.S.,
Liang, S.,
Yang, K.,
Beware of per-pixel characterization of land cover,
JRS(21), No. 4, March 2000, pp. 839.
Apply mixture models
0002
BibRef
Faraklioti, M.,
Petrou, M.,
Recovering more classes than available bands for sets of mixed pixels
in satellite images,
IVC(18), No. 9, June 2000, pp. 705-713.
Elsevier DOI
0004
BibRef
Earlier:
Recovering More Classes than Available Bands for Mixed Pixels
in Remote Sensing,
BMVC98(xx-yy).
BibRef
Faraklioti, M.,
Petrou, M.,
Illumination invariant unmixing of sets of mixed pixels,
GeoRS(39), No. 10, October 2001, pp. 2227-2234.
IEEE Top Reference.
0111
BibRef
Camilleri, K.P.[Kenneth P.],
Petrou, M.[Maria],
Spectral Unmixing of Mixed Pixels for Texture Boundary Refinement,
ICPR00(Vol III: 1084-1087).
IEEE DOI
0009
BibRef
Hsieh, P.F.[Pi-Fuei],
Lee, L.C.,
Chen, N.Y.[Nai-Yu],
Effect of spatial resolution on classification errors of pure and mixed
pixels in remote sensing,
GeoRS(39), No. 12, December 2001, pp. 2657-2663.
IEEE Top Reference.
0201
BibRef
Kustas, W.P.[William P.],
Norman, J.M.[John M.],
Evaluating the Effects of Subpixel Heterogeneity on
Pixel Average Fluxes,
RSE(74), No. 3, 2000, pp. 327- 342.
0102
BibRef
Cihlar, J.,
Du, Y.[Yong],
Latifovic, R.,
Land cover dependence in the detection of contaminated pixels in
satellite optical data,
GeoRS(39), No. 5, May 2001, pp. 1084-1094.
IEEE Top Reference.
0106
BibRef
Manolakis, D.,
Siracusa, C.,
Shaw, G.,
Hyperspectral subpixel target detection using the linear mixing model,
GeoRS(39), No. 7, July 2001, pp. 1392-1409.
IEEE Top Reference.
0108
BibRef
Rosin, P.L.,
Robust pixel unmixing,
GeoRS(39), No. 9, September 2001, pp. 1978-1983.
IEEE Top Reference.
PDF File.
0111
BibRef
Ju, J.C.[Jun-Chang],
Kolaczyk, E.D.[Eric D.],
Gopal, S.[Sucharita],
Gaussian mixture discriminant analysis and sub-pixel land cover
characterization in remote sensing,
RSE(84), No. 4, 10 April 2003, pp. 550-560.
Elsevier DOI
0309
BibRef
Kolaczyk, E.D.,
On the use of prior and posterior information in the subpixel
proportion problem,
GeoRS(41), No. 11, November 2003, pp. 2687-2691.
IEEE Abstract.
0311
BibRef
Miao, L.[Lidan],
Qi, H.R.[Hai-Rong],
Szu, H.[Harold],
A Maximum Entropy Approach to Unsupervised Mixed-Pixel Decomposition,
IP(16), No. 4, April 2007, pp. 1008-1021.
IEEE DOI
0704
BibRef
Earlier:
Unsupervised Decomposition of Mixed Pixels Using the Maximum Entropy
Principle,
ICPR06(I: 1067-1070).
IEEE DOI
0609
BibRef
Viswanath, P.,
Babu, V.S.[V. Suresh],
Rough-DBSCAN: A fast hybrid density based clustering method for large
data sets,
PRL(30), No. 16, 1 December 2009, pp. 1477-1488.
Elsevier DOI
0911
Clustering; Density based clustering; DBSCAN; Leaders; Rough sets
BibRef
Viswanath, P.,
Pinkesh, R.[Rajwala],
l-DBSCAN: A Fast Hybrid Density Based Clustering Method,
ICPR06(I: 912-915).
IEEE DOI
0609
BibRef
Kasetkasem, T.,
Arora, M.K.[Manoj K.],
Varshney, P.K.[Pramod K.],
Areekul, V.,
Improving Subpixel Classification by Incorporating Prior Information in
Linear Mixture Models,
GeoRS(49), No. 3, March 2011, pp. 1001-1013.
IEEE DOI
1103
BibRef
Zhong, Y.F.[Yan-Fei],
Zhang, L.P.[Liang-Pei],
Remote Sensing Image Subpixel Mapping Based on Adaptive Differential
Evolution,
SMC-B(42), No. 5, October 2012, pp. 1306-1329.
IEEE DOI
1209
BibRef
Earlier:
Adaptive Multi-Objective Sub-Pixel Mapping Framework Based on Memetic
Algorithm for Hyperspectral Remote Sensing Imagery,
AnnalsPRS(I-7), No. 2012, pp. 191-196.
DOI Link
1209
See also Adaptive Artificial Immune Network for Supervised Classification of Multi-/Hyperspectral Remote Sensing Imagery, An.
BibRef
Zhong, Y.F.[Yan-Fei],
Wu, Y.Y.[Yun-Yun],
Zhang, L.P.[Liang-Pei],
Xu, X.[Xiong],
Adaptive MAP Sub-Pixel Mapping Model Based on Regularization Curve
for Multiple Shifted Hyperspectral Imagery,
PandRS(96), No. 1, 2014, pp. 134-148.
Elsevier DOI
1410
Hyperspectral image
BibRef
Xu, X.[Xiong],
Tong, X.H.[Xiao-Hua],
Plaza, A.[Antonio],
Li, J.[Jun],
Zhong, Y.F.[Yan-Fei],
Xie, H.[Huan],
Zhang, L.P.[Liang-Pei],
A New Spectral-Spatial Sub-Pixel Mapping Model for Remotely Sensed
Hyperspectral Imagery,
GeoRS(56), No. 11, November 2018, pp. 6763-6778.
IEEE DOI
1811
Hyperspectral imaging, Genetic algorithms, Image resolution,
Linear programming, Neural networks, Hyperspectral imaging,
super-resolution mapping
BibRef
Tong, X.,
Zhang, X.,
Shan, J.,
Xie, H.,
Liu, M.,
Attraction-Repulsion Model-Based Subpixel Mapping of
Multi-/Hyperspectral Imagery,
GeoRS(51), No. 5, May 2013, pp. 2799-2814.
IEEE DOI
1305
BibRef
Xu, X.[Xiong],
Zhong, Y.F.[Yan-Fei],
Zhang, L.P.[Liang-Pei],
Adaptive Subpixel Mapping Based on a Multiagent System for
Remote-Sensing Imagery,
GeoRS(52), No. 2, February 2014, pp. 787-804.
IEEE DOI
1402
geophysical image processing
BibRef
Zhong, Y.F.[Yan-Fei],
Wu, Y.Y.[Yun-Yun],
Xu, X.[Xiong],
Zhang, L.P.[Liang-Pei],
An Adaptive Subpixel Mapping Method Based on MAP Model and Class
Determination Strategy for Hyperspectral Remote Sensing Imagery,
GeoRS(53), No. 3, March 2015, pp. 1411-1426.
IEEE DOI
1402
geophysical image processing
BibRef
Feng, R.[Ruyi],
Zhong, Y.F.[Yan-Fei],
Wu, Y.Y.[Yun-Yun],
He, D.[Da],
Xu, X.[Xiong],
Zhang, L.P.[Liang-Pei],
Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote
Sensing Imagery,
RS(8), No. 3, 2016, pp. 250.
DOI Link
1604
See also Multiobjective Subpixel Land-Cover Mapping.
BibRef
Feng, R.[Ruyi],
Zhong, Y.F.[Yan-Fei],
Xu, X.[Xiong],
Zhang, L.P.[Liang-Pei],
Adaptive Sparse Subpixel Mapping With a Total Variation Model for
Remote Sensing Imagery,
GeoRS(54), No. 5, May 2016, pp. 2855-2872.
IEEE DOI
1604
image processing
BibRef
Song, M.[Mi],
Zhong, Y.F.[Yan-Fei],
Ma, A.L.[Ai-Long],
Feng, R.[Ruyi],
Multiobjective Sparse Subpixel Mapping for Remote Sensing Imagery,
GeoRS(57), No. 7, July 2019, pp. 4490-4508.
IEEE DOI
1907
Dictionaries, Remote sensing, Correlation, Graphical models,
Distribution functions, Optimization, Evolutionary computation,
subpixel mapping (SPM)
BibRef
Xu, X.[Xiong],
Tong, X.H.[Xiao-Hua],
Plaza, A.[Antonio],
Zhong, Y.F.[Yan-Fei],
Xie, H.[Huan],
Zhang, L.P.[Liang-Pei],
Joint Sparse Sub-Pixel Mapping Model with Endmember Variability for
Remotely Sensed Imagery,
RS(9), No. 1, 2017, pp. xx-yy.
DOI Link
1702
BibRef
Ma, A.L.[Ai-Long],
Zhong, Y.F.[Yan-Fei],
Zhang, L.P.[Liang-Pei],
Adaptive Multiobjective Memetic Fuzzy Clustering Algorithm for Remote
Sensing Imagery,
GeoRS(53), No. 8, August 2015, pp. 4202-4217.
IEEE DOI
1506
fuzzy systems
BibRef
Zhong, Y.F.[Yan-Fei],
Zhang, L.P.[Liang-Pei],
Sub-Pixel Mapping Based on Artificial Immune Systems for Remote Sensing
Imagery,
PR(46), No. 11, November 2013, pp. 2902-2926.
Elsevier DOI
1306
Sub-pixel mapping; Remote sensing; Artificial immune systems;
Clonal selection; Classification
BibRef
Xie, Z.P.[Zhen-Ping],
Wang, S.T.[Shi-Tong],
Hu, D.[Dewen],
New insight at level set and Gaussian mixture model for natural image
segmentation,
SIViP(7), No. 3, May 2013, pp. 521-536.
WWW Link.
1305
BibRef
Tanaka, M.[Masayuki],
Okutomi, M.[Masatoshi],
Neighbor Pixel Mixture,
ICPR06(III: 647-650).
IEEE DOI
0609
BibRef
Chan, J.C.W.[Jonathan C.W.],
Defries, R.S.[Ruth S.],
Townshend, J.R.G.[John R.G.],
Improved Recognition of Spectrally Mixed Land Cover Classes Using
Spatial Textures and Voting Classifications,
CAIP01(217 ff.).
Springer DOI
0210
BibRef
Chapter on Pattern Recognition, Clustering, Statistics, Grammars, Learning, Neural Nets, Genetic Algorithms continues in
Detecting Clusters and Number of Clusters, Number of Classes .