GPU-Accelerated CatBoost-Forest for Hyperspectral Image Classification Via Parallelized mRMR Ensemble Subspace Feature Selection

GPU-Accelerated CatBoost-Forest for Hyperspectral Image Classification Via Parallelized mRMR Ensemble Subspace Feature Selection

In this article, the graphics processing unit (GPU)-accelerated CatBoost (GPU-CatBoost) algorithm for hyperspectral image classification is first introduced and comparatively studied using diverse features. To further foster the classification performance from both accurate and efficient viewpoints,...

Full description

Bibliographic Details
Main Authors: Alim Samat, Erzhu Li, Peijun Du, Sicong Liu, Junshi Xia
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
CatBoost
ensemble learning (EL)
feature selection (FS)
gradient boosted decision tree (GBDT)
histogram-based gradient boosting trees (histGBT)
hyperspectral image classification
Online Access:https://ieeexplore.ieee.org/document/9368975/

Internet

https://ieeexplore.ieee.org/document/9368975/

Similar Items