A Novel Object-Based Supervised Classification Method with Active Learning and Random Forest for PolSAR Imagery

A Novel Object-Based Supervised Classification Method with Active Learning and Random Forest for PolSAR Imagery

Most of the traditional supervised classification methods using full-polarimetric synthetic aperture radar (PolSAR) imagery are dependent on sufficient training samples, whereas the results of pixel-based supervised classification methods show a high false alarm rate due to the influence of speckle...

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Bibliographic Details
Main Authors: Wensong Liu, Jie Yang, Pingxiang Li, Yue Han, Jinqi Zhao, Hongtao Shi
Format: Article
Language:English
Published: MDPI AG 2018-07-01
Series:Remote Sensing
Subjects:
PolSAR imagery
object-based classification
generalized statistical region merging (GSRM)
active learning (AL)
random forest (RF)
Online Access:http://www.mdpi.com/2072-4292/10/7/1092

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http://www.mdpi.com/2072-4292/10/7/1092

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