GCN: GPU-Based Cube CNN Framework for Hyperspectral Image Classification

Abstract

Hyperspectral image classification has been proved significant in remote sensing field. Traditional classification methods have meet bottlenecks due to the lack of remote sensing background knowledge or high dimensionality. Deep learning based methods, such as deep convolutional neural network (CNN), can effectively extract high level features from raw data. But the training of deep CNN is rather time-consuming. The general purpose graphic processing units (GPUs) have been considered as one of the most common co-processors that can help accelerate deep learning applications. In this paper we propose a GPU-based Cube CNN (GCN) framework for hyperspectral image classification. First, a Parallel Neighbor Pixels Extraction (PNPE) algorithm is designed to enable the framework directly loading raw hyperspectral image into GPU's global memory, and extracting samples into data cube. Then, based on the peculiarity of hyperspectral image and cube convolution, we propose a novel Cube CNN-to-GPU mapping mechanism that transfers the training of Cube CNN to GPU effectively. Finally, the mini-batch gradient descent(MBGD) algorithm is improved with Computing United Device Architecture(CUDA) multi-streaming technique, which further speeds up network training in GCN framework. Experiments on KSC dataset, PU dataset and SA dataset show that, compared with state-of-art framework Caffe, we achieve up to 83% and 67% reduction in network training time without losing accuracy, when using SGD (Stochastic Gradient Descent) and MBGD algorithm respectively. Experiments across different GPUs show the same performance trend, which demonstrates the good extendibility of GCN framework.