14.2.8.3 Mixed Pixels, Subpixel Classification

Chapter Contents (Back)
Mixed Pixels. 9905
subpixel

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
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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.
HTML Version. 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.[Yunyun], 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.[Yunyun], 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

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.[Ailong], 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

Ma, A.[Ailong], Zhong, Y.F.[Yan-Fei], Zhang, L.P.[Liang-Pei],
Spectral-Spatial Clustering with a Local Weight Parameter Determination Method for Remote Sensing Imagery,
RS(8), No. 2, 2016, pp. 124.
DOI Link 1603
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


Jenzri, H.[Hamdi], Frigui, H.[Hichem], Gader, P.[Paul],
Context dependent hyperspectral subpixel target detection,
ICIP14(5062-5066)
IEEE DOI 1502
Context 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 .


Last update:Nov 17, 2018 at 09:12:27