Extracting the Forest Type From Remote Sensing Images by Random Forest

• Published in: IEEE sensors journal Vol. 21; no. 16; pp. 17447 - 17454
• Main Authors: Linhui, Li, Weipeng, Jing, Huihui, Wang
• Format: Journal Article
• Language: English
• Published: New York IEEE 15.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Classification; Color imagery; Decision trees; Feature extraction; Forest type extraction; Forestry; Identification methods; Image resolution; Image segmentation; object oriented; Random forests; Remote sensing; RF classification; Sensors; Spatial data; Support vector machines; SVM classification; Vegetation; Vegetation index; Vegetation mapping

Identifying the types of forest and the corresponding distribution is of significance in forest resource monitoring and management. Considering the low accuracy of extracting the information of forest types from high-resolution remote sensing images and the lack of an effective identification method...