The Integration of Multi-Source Remotely Sensed Data with Hierarchically Based Classification Approaches in Support of the Classification of Wetlands

Canadian Journal of Remote Sensing ◽

10.1080/07038992.2021.1967732 ◽

2021 ◽

pp. 1-24

Author(s):

Aaron Judah ◽

Baoxin Hu

Keyword(s):

Remotely Sensed ◽

Remotely Sensed Data

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Active Learning: Any Value for Classification of Remotely Sensed Data?

Proceedings of the IEEE ◽

10.1109/jproc.2012.2231951 ◽

2013 ◽

Vol 101 (3) ◽

pp. 593-608 ◽

Author(s):

Melba M. Crawford ◽

Devis Tuia ◽

Hsiuhan Lexie Yang

Keyword(s):

Active Learning ◽

Remotely Sensed ◽

Remotely Sensed Data

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Simulation Comparison of Methods for Bayesian Contextual Classification of Remotely Sensed Data

Bayesian Analysis in Statistics and Econometrics - Lecture Notes in Statistics ◽

10.1007/978-1-4612-2944-5_9 ◽

1992 ◽

pp. 163-175 ◽

Author(s):

Ruben Klein ◽

S. James Press

Keyword(s):

Remotely Sensed ◽

Comparison Of Methods ◽

Remotely Sensed Data

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Land-cover classification of remotely sensed data using Kalman filtering.

Doboku Gakkai Ronbunshu ◽

10.2208/jscej.1997.576_123 ◽

1997 ◽

pp. 123-131 ◽

Author(s):

Iwao Okutani ◽

Haoxiang Wu

Keyword(s):

Kalman Filtering ◽

Land Cover Classification ◽

Remotely Sensed ◽

Remotely Sensed Data

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Integration of domain knowledge in the form of ancillary map data into supervised classification of remotely sensed data

IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2002.1025768 ◽

2003 ◽

Author(s):

M.A. Friedl ◽

D.K. McIver ◽

C.E. Brodley

Keyword(s):

Domain Knowledge ◽

Supervised Classification ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

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An artificial neural network (ANN) based software package for classification of remotely sensed data

International Journal of Rock Mechanics and Mining Sciences & Geomechanics Abstracts ◽

10.1016/s0148-9062(97)87468-2 ◽

1996 ◽

Vol 33 (8) ◽

pp. A364

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Software Package ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Supervised classification of remotely sensed data with ongoing learning capability

International Journal of Remote Sensing ◽

10.1080/01431160110087944 ◽

2002 ◽

Vol 23 (22) ◽

pp. 4965-4970 ◽

Author(s):

R. Barandela ◽

M. Juarez

Keyword(s):

Supervised Classification ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Learning Capability

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Feature Evolution for Classification of Remotely Sensed Data

IEEE Geoscience and Remote Sensing Letters ◽

10.1109/lgrs.2007.895285 ◽

2007 ◽

Vol 4 (3) ◽

pp. 354-358 ◽

Author(s):

Demetris Stathakis ◽

Kostas Perakis

Keyword(s):

Remotely Sensed ◽

Remotely Sensed Data ◽

Feature Evolution

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An artificial neural network (ANN) based software package for classification of remotely sensed data

Computers & Geosciences ◽

10.1016/0098-3004(95)00059-3 ◽

1996 ◽

Vol 22 (1) ◽

pp. 81-87 ◽

Author(s):

K.K. Mohanty ◽

T.J. Majumdar

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Software Package ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Classification of Pinus massoniana and secondary deciduous tree species in northern subtropical region based on high resolution and hyperspectral remotely sensed data

Chinese Journal of Plant Ecology ◽

10.17521/cjpe.2015.0109 ◽

2015 ◽

Vol 39 (12) ◽

pp. 1125-1135

Author(s):

SHEN Xin ◽

◽

CAO Lin ◽

XU Ting ◽

and SHE Guang-Hui

Keyword(s):

High Resolution ◽

Tree Species ◽

Pinus Massoniana ◽

Remotely Sensed ◽

Deciduous Tree ◽

Subtropical Region ◽

Remotely Sensed Data

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Use of Spot Imagery for Estimating Surface Runoff in Sudan

FES Journal of Engineering Sciences ◽

10.52981/fjes.v3i1.82 ◽

2008 ◽

Vol 3 (1) ◽

pp. 10

Author(s):

Abdullah S. Alsalman

Keyword(s):

Standard Deviation ◽

Surface Runoff ◽

Remotely Sensed ◽

Remotely Sensed Data ◽

Runoff Volume ◽

Digital classification of remotely sensed data can be very effective in determining volume of runoff. Mean discrepancy value of ± 6 mm between observed and estimated runoff volume was obtained in this experiment with a standard deviation of ±7.2 mm.

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