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

Abstract

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, an ensemble version of GPU-CatBoost, the GPU-accelerated CatBoost-Forest (GPU-CatBF) algorithm, is proposed by adopting the parallelized minimum redundancy maximum relevance (mRMR) ensemble (PmRMRE) feature selection (FS) algorithm. To evaluate the performance and suitability of mRMR and PmRMRE, 11 other state-of-the-art FS algorithms are comprehensively investigated. Experimental results on three widely acknowledged hyperspectral benchmarks showed that: 1) GPU-CatBoost is also an advanced ensemble learning (EL) algorithm for hyperspectral image classification using diverse features; 2) mRMR and PmRMRE have advanced properties for highly discriminative features and band selection, and the best results are achieved by PmRMRE in most cases in terms of both the robustness and computational efficiency; and 3) GPU-CatBF always outperforms CatBoost and GPU-CatBoost, while compatible and even better results are reachable without losing much computational efficiency in contrast with other selected decision tree-based EL algorithms.

Keywords

• CatBoost
• ensemble learning (EL)
• feature selection (FS)
• gradient boosted decision tree (GBDT)
• histogram-based gradient boosting trees (histGBT)
• hyperspectral image classification