Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks

Fully Connected Conditional Random Fields for High-Resolution Remote Sensing Land Use/Land Cover Classification with Convolutional Neural Networks

The interpretation of land use and land cover (LULC) is an important issue in the fields of high-resolution remote sensing (RS) image processing and land resource management. Fully training a new or existing convolutional neural network (CNN) architecture for LULC classification requires a large amo...

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Bibliographic Details
Main Authors: Bin Zhang, Cunpeng Wang, Yonglin Shen, Yueyan Liu
Format: Article
Language:English
Published: MDPI AG 2018-11-01
Series:Remote Sensing
Subjects:
remote sensing
image classification
fully connected conditional random fields (FC-CRF)
convolutional neural networks (CNN)
Online Access:https://www.mdpi.com/2072-4292/10/12/1889

Internet

https://www.mdpi.com/2072-4292/10/12/1889

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