(Q56754531)

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The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classifications with Imperfect Reference Data

scholarly article by Giles Foody et al published 1 November 2016 in International Journal of Geo Information

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scholarly article

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The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classifications with Imperfect Reference Data (English)

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Duccio Rocchini

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Giles Foody

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Giles Foody

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1

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Lucy Bastin

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Lucy Bastin

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5

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Mahesh Pal

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2

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Carol Garzon-Lopez

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4

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publication date

1 November 2016

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published in

International Journal of Geo Information

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volume

5

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issue

11

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page(s)

199

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copyright license

Creative Commons Attribution 4.0 International

start time

1 November 2016

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A survey of image classification methods and techniques for improving classification performance

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Support-vector networks

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Enrichment of high-throughput screening data with increasing levels of noise using support vector machines, recursive partitioning, and laplacian-modified naive bayesian classifiers

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21 January 2018

Sparse multinomial logistic regression: fast algorithms and generalization bounds.

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21 January 2018

Tree Species Abundance Predictions in a Tropical Agricultural Landscape with a Supervised Classification Model and Imbalanced Data

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Global characterization and monitoring of forest cover using Landsat data: opportunities and challenges

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Identifiers

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10.3390/IJGI5110199

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journals/ijgi/FoodyPRGB16

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DBLP Dataset 2021-01-02

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28 January 2021

(Q56754531)

The Sensitivity of Mapping Methods to Reference Data Quality: Training Supervised Image Classifications with Imperfect Reference Data

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