Parallel collaborative representation for hyperspectral image classification on GPUs

Abstract

Collaborative representation-based classification with distance-weighted Tikhonov regularization (CRT) has offered high accuracy and efficiency. Due to its per-pixel classification nature without a training step, this paper develops a parallel implementation by using compute unified device architecture (CUDA) on graphics processing units (GPUs). To further improve classification accuracy, local binary pattern (LBP) is used for spatial feature extraction, and an unsupervised band selections approach is applied for dimensionality reduction and an optimized collaborative model combining spatial-spectral features is employed. The proposed parallel implementation is able to increase computational efficiency while not degrading classification accuracy when compared with the serial implementations on central processing units (CPUs).