Hyperspectral imagery classification based on semi-supervised 3-D deep neural network and adaptive band selection

作者：

Highlights：

• Adaptive Dimensionality Reduction for the selection of relevant spectral bands.

• Selecting the most relevant spectral bands using limited number of training samples.

• Semi Supervised 3D Convolutional Neural Network for image classification.

• Extracting deep spectral and spatial features based on convolutional encoder-decoder.

• Enhancing image classification compared to well-established deep learning methods.

摘要

•Adaptive Dimensionality Reduction for the selection of relevant spectral bands.•Selecting the most relevant spectral bands using limited number of training samples.•Semi Supervised 3D Convolutional Neural Network for image classification.•Extracting deep spectral and spatial features based on convolutional encoder-decoder.•Enhancing image classification compared to well-established deep learning methods.

论文关键词：Hyperspectral imagery classification,Convolutional neural network (CNN),Adaptive dimensionality reduction,Deep learning

论文评审过程：Received 10 October 2018, Revised 18 March 2019, Accepted 4 April 2019, Available online 5 April 2019, Version of Record 13 April 2019.

论文官网地址：https://doi.org/10.1016/j.eswa.2019.04.006