The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy

The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy

Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robu...

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Bibliographic Details
Main Authors: Sornkitja Boonprong, Chunxiang Cao, Wei Chen, Xiliang Ni, Min Xu, Bipin Kumar Acharya
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:ISPRS International Journal of Geo-Information
Subjects:
machine learning
remote sensing
robustness
Gaussian noise
salt–pepper noise
multiplicative noise
Online Access:http://www.mdpi.com/2220-9964/7/7/274

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http://www.mdpi.com/2220-9964/7/7/274

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