Dual-Concentrated Network With Morphological Features for Tree Species Classification Using Hyperspectral Image
Author:
Affiliation:
1. School of Information and Electronics, Beijing Institute of Technology, Beijing, China
2. Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China
Funder
National Natural Science Foundation of China
China Postdoctoral Science Foundation
National Key Research and Development Program of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Atmospheric Science,Computers in Earth Sciences
Link
http://xplorestaging.ieee.org/ielx7/4609443/9656571/09860044.pdf?arnumber=9860044
Reference42 articles.
1. A Novel Endmember Bundle Extraction and Clustering Approach for Capturing Spectral Variability Within Endmember Classes
2. Diverse Region-Based CNN for Hyperspectral Image Classification
3. Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Dimension Reduction and Deep Learning Approach
4. Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
5. Comparison of support vector machine, random forest and neural network classifiers for tree species classification on airborne hyperspectral APEX images
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3. Using UAV LiDAR Intensity Frequency and Hyperspectral Features to Improve the Accuracy of Urban Tree Species Classification;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024
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