Title
A Systematic Classification Method for Grassland Community Division Using China's ZY1-02D Hyperspectral Observations
Abstract
The main feature of grassland degradation is the change in the vegetation community structure. Hyperspectral-based grassland community identification is the basis and a prerequisite for large-area high-precision grassland degradation monitoring and management. To obtain the distribution pattern of grassland communities in Xilinhot, Inner Mongolia Autonomous Region, China, we propose a systematic classification method (SCM) for hyperspectral grassland community identification using China's ZiYuan 1-02D (ZY1-02D) satellite. First, the sample label data were selected from the field-collected samples, vegetation map data, and function zoning data for the Nature Reserve. Second, the spatial features of the images were extracted using extended morphological profiles (EMPs) based on the reduced dimensionality of principal component analysis (PCA). Then, they were input into the random forest (RF) classifier to obtain the preclassification results for grassland communities. Finally, to reduce the influence of salt-and-pepper noise, the label similarity probability filter (LSPF) method was used for postclassification processing, and the RF was again used to obtain the final classification results. The results showed that, compared with the other seven (e.g., SVM, RF, 3D-CNN) methods, the SCM obtained the optimal classification results with an overall classification accuracy (OCA) of 94.56%. In addition, the mapping results of the SCM showed its ability to accurately identify various ground objects in large-scale grassland community scenes.
Year
DOI
Venue
2022
10.3390/rs14153751
REMOTE SENSING
Keywords
DocType
Volume
grassland communities, hyperspectral remote sensing, systematic classification method
Journal
14
Issue
ISSN
Citations 
15
2072-4292
0
PageRank 
References 
Authors
0.34
0
8
Name
Order
Citations
PageRank
Dandan Wei100.34
Kai Liu2296.95
Chenchao Xiao300.34
Weiwei Sun415.75
Weiwei Liu500.68
Lidong Liu600.34
Xizhi Huang700.34
Chunyong Feng800.34