Feature-Level Fusion of Polarized SAR and Optical Images Based on Random Forest and Conditional Random Fields

Feature-Level Fusion of Polarized SAR and Optical Images Based on Random Forest and Conditional Random Fields

In terms of land cover classification, optical images have been proven to have good classification performance. Synthetic Aperture Radar (SAR) has the characteristics of working all-time and all-weather. It has more significant advantages over optical images for the recognition of some scenes, such...

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Bibliographic Details
Main Authors: Yingying Kong, Biyuan Yan, Yanjuan Liu, Henry Leung, Xiangyang Peng
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Remote Sensing
Subjects:
polarized SAR
optical image
random forest
conditional random fields
feature-level fusion
Online Access:https://www.mdpi.com/2072-4292/13/7/1323

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