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Open AccessArticle

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

1
University of the Chinese Academy of Sciences, Beijing 100094, China
2
State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2018, 7(7), 274; https://doi.org/10.3390/ijgi7070274
Received: 8 May 2018 / Revised: 12 June 2018 / Accepted: 26 June 2018 / Published: 12 July 2018
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 robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users’ actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt–pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data. View Full-Text
Keywords: machine learning; remote sensing; robustness; Gaussian noise; salt–pepper noise; multiplicative noise machine learning; remote sensing; robustness; Gaussian noise; salt–pepper noise; multiplicative noise
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Boonprong, S.; Cao, C.; Chen, W.; Ni, X.; Xu, M.; Acharya, B.K. The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy. ISPRS Int. J. Geo-Inf. 2018, 7, 274.

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ISPRS Int. J. Geo-Inf., EISSN 2220-9964, Published by MDPI AG
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