Scalable recurrent neural network for hyperspectral image classification
The Journal of Supercomputing(2020)
摘要
Hyperspectral imaging (HSI) collects hundreds of images over large spatial observation areas on the Earth’s surface, recording scenes at different wavelength channels and providing a vast amount of information. Recurrent neural networks (RNNs) have been widely used for the classification of HSI datasets, understood as a single sequence of pixel vectors with high dimensionality. However, the RNN model scales poorly when dealing with HSI scenes with large dimensionality. In order to mitigate this problem, this paper presents a new RNN classifier based on simple recurrent units that performs HSI classification in a highly scalable and efficient way. Our experimental results (conducted on four real HSI datasets) reveal very good performance, not only in terms of classification accuracy (in line with existing methods), but also in terms of computational performance when dealing with large datasets.
更多查看译文
关键词
Hyperspectral image, Recurrent neural networks, CUDA
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络