Combining Object-Oriented and Deep Learning Methods to Estimate Photosynthetic and Non-Photosynthetic Vegetation Cover in the Desert from Unmanned Aerial Vehicle Images with Consideration of Shadows

Combining Object-Oriented and Deep Learning Methods to Estimate Photosynthetic and Non-Photosynthetic Vegetation Cover in the Desert from Unmanned Aerial Vehicle Images with Consideration of Shadows

Soil erosion is a global environmental problem. The rapid monitoring of the coverage changes in and spatial patterns of photosynthetic vegetation (PV) and non-photosynthetic vegetation (NPV) at regional scales can help improve the accuracy of soil erosion evaluations. Three deep learning semantic se...

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Bibliographic Details
Main Authors: Jie He, Du Lyu, Liang He, Yujie Zhang, Xiaoming Xu, Haijie Yi, Qilong Tian, Baoyuan Liu, Xiaoping Zhang
Format: Article
Language:English
Published: MDPI AG 2022-12-01
Series:Remote Sensing
Subjects:
unmanned aerial vehicle (UAV) images
PV/NPV classification
shaded features
deep learning semantic segmentation
object-oriented technique
desert
Online Access:https://www.mdpi.com/2072-4292/15/1/105

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