A Hyperspectral Image Classification Approach Based on Feature Fusion and Multi-Layered Gradient Boosting Decision Trees

Entropy

Wenjie Chen¹  Size Liu²  Zhu Xiao³  Hua Wang⁴  Shenyuan Xu⁴  Fan Zhang⁴ 

[1] Business College, Central South University of Forestry and Technology, Changsha 410004, China;College of Communication Engineering, Xidian University, Xi’an 710071, China;College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China;State Key Laboratory of Geo-Information Engineering, Xi’an 710054, China;

关键词: hyperspectral image;    multi-layered gradient boosting decision trees (mGBDTs);    feature fusion;    image classification;   

DOI  :  10.3390/e23010020

来源: DOAJ

【 摘 要 】

At present, many Deep Neural Network (DNN) methods have been widely used for hyperspectral image classification. Promising classification results have been obtained by utilizing such models. However, due to the complexity and depth of the model, increasing the number of model parameters may lead to an overfitting of the model, especially when training data are insufficient. As the performance of the model mainly depends on sufficient data and a large network with reasonably optimized hyperparameters, using DNNs for classification requires better hardware conditions and sufficient training time. This paper proposes a feature fusion and multi-layered gradient boosting decision tree model (FF-DT) for hyperspectral image classification. First, we fuse extended morphology profiles (EMPs), linear multi-scale spatial characteristics, and nonlinear multi-scale spatial characteristics as final features to extract both special and spectral features. Furthermore, a multi-layered gradient boosting decision tree model is constructed for classification. We conduct experiments based on three datasets, which in this paper are referred to as the Pavia University, Indiana Pines, and Salinas datasets. It is shown that the proposed FF-DT achieves better performance in classification accuracy, training conditions, and time consumption than other current classical hyperspectral image classification methods.

【 授权许可】

Unknown