Chang, C.,
Heinz, D.C.,
Constrained Subpixel Target Detection for Remotely Sensed Imagery,
GeoRS(38), No. 3, May 2000, pp. 1144-1159.
IEEE Top Reference.
0006
BibRef
Bruce, L.M.,
Morgan, C.,
Larsen, S.,
Automated detection of subpixel hyperspectral targets with continuous
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GeoRS(39), No. 10, October 2001, pp. 2217-2226.
IEEE Top Reference.
0111
BibRef
Bruce, L.M.,
Li, J.[Jiang],
Huang, Y.[Yan],
Automated detection of subpixel hyperspectral targets with adaptive
multichannel discrete wavelet transform,
GeoRS(40), No. 4, April 2002, pp. 977-980.
IEEE Top Reference.
0206
BibRef
Saura, S.[Santiago],
Castro, S.[Sandra],
Scaling functions for landscape pattern metrics derived from remotely
sensed data: Are their subpixel estimates really accurate?,
PandRS(62), No. 3, August 2007, pp. 201-216.
Elsevier DOI
0709
Scale; Landscape pattern; Sensor spatial resolution; Spatial metrics;
Landscape ecology; Land cover analysis
BibRef
Makido, Y.[Yasuyo],
Shortridge, A.[Ashton],
Weighting Function Alternatives for a Subpixel Allocation Model,
PhEngRS(73), No. 11, November 2007, pp. 1233-1240.
WWW Link.
0709
Properties of a pixel-swapping optimization algorithm for predicting subpixel
land-cover distribution are investigated, and improvements to it are evaluated.
BibRef
Walton, J.T.[Jeffrey T.],
Subpixel Urban Land Cover Estimation:
Comparing Cubist, Random Forests, and Support Vector Regression,
PhEngRS(74), No. 10, October 2008, pp. 1213-1222.
WWW Link.
0804
Three machine learning subpixel estimation methods were applied to
estimate urban cover and the resulting predictions were compared based
on accuracy.
BibRef
Shen, Z.Q.[Zhang-Quan],
Qi, J.G.[Jia-Guo],
Wang, K.[Ke],
Modification of Pixel-swapping Algorithm with Initialization from a
Sub-pixel/pixel Spatial Attraction Model,
PhEngRS(75), No. 5, May 2009, pp. 557-568.
WWW Link.
0904
Based on the pixel-swapping algorithm, its initialization process is
replaced by a sub-pixel mapping approach with a subpixel/ pixel
spatial attraction model; the modified algorithm can improve sub-pixel
mapping accuracy and computation efficiency.
BibRef
Ge, Y.,
Li, S.,
Lakhan, V.C.,
Development and Testing of a Subpixel Mapping Algorithm,
GeoRS(47), No. 7, July 2009, pp. 2155-2164.
IEEE DOI
0906
BibRef
Wang, Q.M.[Qun-Ming],
Shi, W.Z.[Wen-Zhong],
Wang, L.,
Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in
Units of Class,
GeoRS(52), No. 5, May 2014, pp. 2940-2959.
IEEE DOI
1403
Mathematical model.
Subpixel mapping.
See also Spatiotemporal Subpixel Mapping of Time-Series Images.
BibRef
Chang, C.I.[C. I],
Ren, H.,
An Experiment-Based Quantitative and Comparative Analysis of Target
Detection and Image Classification Algorithms for Hyperspectral Imagery,
GeoRS(38), No. 2, March 2000, pp. 1044-1063.
IEEE Top Reference.
0004
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Wang, Y.,
Lee, L.C.,
Xue, B.,
Wang, L.,
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Yu, C.,
Li, S.,
Chang, C.I.,
A Posteriori Hyperspectral Anomaly Detection for Unlabeled
Classification,
GeoRS(56), No. 6, June 2018, pp. 3091-3106.
IEEE DOI
1806
Anomaly detection, Computer science, Correlation, Detectors,
Hyperspectral imaging, Object detection,
unlabeled anomaly classification (UAC)
BibRef
Xue, B.,
Yu, C.,
Wang, Y.,
Song, M.,
Li, S.,
Wang, L.,
Chen, H.M.,
Chang, C.I.,
A Subpixel Target Detection Approach to Hyperspectral Image
Classification,
GeoRS(55), No. 9, September 2017, pp. 5093-5114.
IEEE DOI
1709
hyperspectral imaging, image classification, iterative methods,
support vector machines,
Gaussian filters, Otsu method, band selection,
constrained energy minimization,
nonlinear band expansion, spatial information,
BibRef
Chen, Y.H.[Yue-Hong],
Ge, Y.[Yong],
Chen, Y.[Yu],
Jin, Y.[Yan],
An, R.[Ru],
Subpixel Land Cover Mapping Using Multiscale Spatial Dependence,
GeoRS(56), No. 9, September 2018, pp. 5097-5106.
IEEE DOI
1809
Remote sensing, Optimization, Spatial resolution,
Image segmentation, Feature extraction, Graphical models,
subpixel mapping (SPM)
See also Spatial-Temporal Super-Resolution Land Cover Mapping With a Local Spatial-Temporal Dependence Model.
BibRef
Ge, Y.[Yong],
Jiang, Y.[Yu],
Chen, Y.H.[Yue-Hong],
Stein, A.[Alfred],
Jiang, D.[Dong],
Jia, Y.X.[Yuan-Xin],
Designing an Experiment to Investigate Subpixel Mapping as an
Alternative Method to Obtain Land Use/Land Cover Maps,
RS(8), No. 5, 2016, pp. 360.
DOI Link
1606
BibRef
Kumar, U.[Uttam],
Ganguly, S.[Sangram],
Nemani, R.R.[Ramakrishna R.],
Raja, K.S.[Kumar S],
Milesi, C.[Cristina],
Sinha, R.[Ruchita],
Michaelis, A.[Andrew],
Votava, P.[Petr],
Hashimoto, H.[Hirofumi],
Li, S.[Shuang],
Wang, W.[Weile],
Kalia, S.[Subodh],
Gayaka, S.[Shreekant],
Exploring Subpixel Learning Algorithms for Estimating Global Land
Cover Fractions from Satellite Data Using High Performance Computing,
RS(9), No. 11, 2017, pp. xx-yy.
DOI Link
1712
BibRef
Lu, L.Z.[Li-Zhen],
Huang, Y.L.[Yan-Lin],
Di, L.P.[Li-Ping],
Hang, D.[Danwei],
A New Spatial Attraction Model for Improving Subpixel Land Cover
Classification,
RS(9), No. 4, 2017, pp. xx-yy.
DOI Link
1705
BibRef
Deng, C.B.[Cheng-Bin],
Li, C.[Chaojun],
Zhu, Z.[Zhe],
Lin, W.[Weiying],
Xi, L.[Li],
Subpixel Urban Impervious Surface Mapping:
The Impact of Input Landsat Images,
PandRS(133), No. Supplement C, 2017, pp. 89-103.
Elsevier DOI
1711
Impervious surface, Random forest, Atmospheric correction,
Seasonality, Multi-temporal images, , Landsat
BibRef
Ma, A.L.[Ai-Long],
Zhong, Y.F.[Yan-Fei],
He, D.[Da],
Zhang, L.P.[Liang-Pei],
Multiobjective Subpixel Land-Cover Mapping,
GeoRS(56), No. 1, January 2018, pp. 422-435.
IEEE DOI
1801
Graphical models, Hyperspectral imaging, Image resolution,
Optimization methods, Multiobjective optimization,
subpixel mapping (SPM)
See also Nonlocal Total Variation Subpixel Mapping for Hyperspectral Remote Sensing Imagery.
BibRef
Yao, W.[Wei],
van Aardt, J.[Jan],
van Leeuwen, M.[Martin],
Kelbe, D.[Dave],
Romanczyk, P.[Paul],
A Simulation-Based Approach to Assess Subpixel Vegetation Structural
Variation Impacts on Global Imaging Spectroscopy,
GeoRS(56), No. 7, July 2018, pp. 4149-4164.
IEEE DOI
1807
geophysical image processing, image resolution, image sensors,
infrared imaging, remote sensing, spectrometers, vegetation,
vegetation structure
BibRef
Yang, X.C.[Xiao-Chen],
Dong, M.Z.[Ming-Zhi],
Wang, Z.Y.[Zi-Yu],
Gao, L.R.[Lian-Ru],
Zhang, L.[Lefei],
Xue, J.H.[Jing-Hao],
Data-augmented matched subspace detector for hyperspectral subpixel
target detection,
PR(106), 2020, pp. 107464.
Elsevier DOI
2006
Hyperspectral imaging, Matched subspace detector (MSD),
Subpixel target detection, Data augmentation
BibRef
Tong, K.[Kang],
Wu, Y.[Yiquan],
Zhou, F.[Fei],
Recent advances in small object detection based on deep learning:
A review,
IVC(97), 2020, pp. 103910.
Elsevier DOI
2005
Small object detection, Deep learning,
Convolutional neural networks
BibRef
Duan, K.,
Du, D.,
Qi, H.,
Huang, Q.,
Detecting Small Objects Using a Channel-Aware Deconvolutional Network,
CirSysVideo(30), No. 6, June 2020, pp. 1639-1652.
IEEE DOI
2006
Object detection, Feature extraction, Training, Birds, Deconvolution,
Proposals, Detectors, Small object detection,
anchor matching
BibRef
Vincent, F.,
Besson, O.,
One-Step Generalized Likelihood Ratio Test for Subpixel Target
Detection in Hyperspectral Imaging,
GeoRS(58), No. 6, June 2020, pp. 4479-4489.
IEEE DOI
2005
Detection, generalized likelihood ratio test (GLRT),
hyperspectral, Kelly, replacement model, subpixel
BibRef
Zhang, X.P.[Xin-Peng],
Wu, J.G.[Ji-Gang],
Peng, Z.H.[Zhi-Hao],
Meng, M.[Min],
SODNet: Small object detection using deconvolutional neural network,
IET-IPR(14), No. 8, 19 June 2020, pp. 1662-1669.
DOI Link
2005
BibRef
Rabbi, J.[Jakaria],
Ray, N.[Nilanjan],
Schubert, M.[Matthias],
Chowdhury, S.[Subir],
Chao, D.[Dennis],
Small-Object Detection in Remote Sensing Images with End-to-End
Edge-Enhanced GAN and Object Detector Network,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link
2005
BibRef
Cheng, X.M.[Xue-Min],
Dong, C.Q.[Chang-Qing],
Ren, Y.[Yong],
Cheng, K.C.[Kai-Chang],
Yan, L.[Lei],
Hu, Y.[Yao],
Hao, Q.[Qun],
Reduced data set for multi-target recognition using compressed
sensing frame,
PRL(129), 2020, pp. 86-91.
Elsevier DOI
2001
Compressed sensing frame, Clustering, Dictionary coding,
Vector space, Multi-target recognition, Tiny feature
BibRef
Kaiser, G.,
Bauer, T.,
Multiscale landscape representation derived from remote sensing images
using spatial subpixel models and combinatorial maps,
OBIA06(xx-yy).
PDF File.
0607
BibRef
Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Irrigation Monitoring, Irrigated Field Detection, Land Use Analysis .