High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

High-Resolution Remote Sensing Data Classification over Urban Areas Using Random Forest Ensemble and Fully Connected Conditional Random Field

As an intermediate step between raw remote sensing data and digital maps, remote sensing data classification has been a challenging and long-standing problem in the remote sensing research community. In this work, an automated and effective supervised classification framework is presented for classi...

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Bibliographic Details
Main Authors: Xiaofeng Sun, Xiangguo Lin, Shuhan Shen, Zhanyi Hu
Format: Article
Language:English
Published: MDPI AG 2017-08-01
Series:ISPRS International Journal of Geo-Information
Subjects:
semantic labeling
random forest
conditional random field
differential morphological profile
ensemble learning
Online Access:https://www.mdpi.com/2220-9964/6/8/245

Internet

https://www.mdpi.com/2220-9964/6/8/245

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