22.1.6.5.1 Subpixel Target, Subpixel Land Use, Tiny Objects

Chapter Contents (Back)
Subpixel.

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 and discrete wavelet transforms,
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
BibRef

Wang, Y., Lee, L.C., Xue, B., Wang, L., Song, M., 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.[Mingzhi], Wang, Z.[Ziyu], Gao, L.[Lianru], 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, Computer vision, 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.[Xinpeng], Wu, J.[Jigang], Peng, Z.[Zhihao], 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


Abubakar, A.J., Hashim, M., Pour, A.B., Shehu, K.,
Multispectral and Hyperspectral Satellite Data for Alteration Mapping Using Partial-Subpixel Algorithm in an Unexplored Region,
GGT19(35-38).
DOI Link 1912
valuate the performance of ASTER and Hyperion data for target detection of hydrothermal zones. 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, Cartography, Aerial Images, Buildings, Roads, Terrain, ATR continues in
Irrigation Monitoring, Irrigated Field Detection, Land Use Analysis .


Last update:Aug 4, 2020 at 13:31:31