小样本高光谱遥感图像深度学习方法

doi:10.16182/j.issn1004731x.joss.201807039

系统仿真学报 ›› 2018, Vol. 30 ›› Issue (7): 2744-2752.doi: 10.16182/j.issn1004731x.joss.201807039

小样本高光谱遥感图像深度学习方法

石祥滨^1,2, 钟健¹, 刘翠微¹, 刘芳¹, 张德园¹

1. 沈阳航空航天大学计算机学院,辽宁沈阳 110136;
2. 辽宁大学信息科学与技术学院,辽宁沈阳 110036

收稿日期:2017-07-31 出版日期:2018-07-10 发布日期:2019-01-08
作者简介:石祥滨(1963-),男,辽宁,博士,教授,研究方向为虚拟现实、视频与图像理解。
基金资助:
国家自然科学基金(61170185, 61602320),辽宁省博士启动基金(201601172,201601180),辽宁省教育厅科学研究一般项目(L2014070,L201607)

Deep Learning Method for Hyperspectral Remote Sensing Images with Small Samples

Shi Xiangbin^1,2, Zhong Jian¹, Liu Cuiwei¹, Liu Fang¹, Zhang Deyuan¹

1. School of Computer, Shenyang Aerospace University, Shenyang 110136, China;
2. College of Information Science and Technology, Liaoning University, Shenyang 110036, China

Received:2017-07-31 Online:2018-07-10 Published:2019-01-08

摘要/Abstract

摘要： 针对高光谱遥感图像光谱信息维度大,标注训练样本较少的问题,提出适合小训练样本的高光谱遥感图像分类框架HSI-CNN,减少模型参数数量的同时保持神经网络深度。通过主成分分析方法进行图像模式不变性以及光谱通道贡献率分析,消除光谱冗余信息。设计了适用于小样本高光谱遥感图像的全卷积神经网络结构,有效降低网络参数数量。提出3种HSI-CNN结构,并对不同结构进行了比较分析。在高光谱遥感数据集Pavia University和Salinas上的实验结果表明,HSI-CNN能够利用少量训练样本有效地提取光谱特征信息,取得了较优的分类性能。

关键词: 高光遥感图像分类, 深度学习, 卷积神经网络, 光谱冗余, 主成分分析

Abstract: In order to solve the problem of large information dimension and fewer labeled training samples of hyperspectral remote sensing images, this paper proposes a hyperspectral remote sensing image classification framework HSI-CNN, which reduces the number of model parameters while maintaining the depth of neural network. Image pattern invariance and spectral channel contribution rate are analyzed, and the spectral redundancy information is reduced by principal component analysis. A full convolution neural network structure suitable for small sample hyperspectral remote sensing images is designed and the amount of network parameters is effectively reduced. Three kinds of HSI-CNN structures are proposed and compared. The experimental results on Pavia University and Salinas hyperspectral remote sensing data sets show that HSI-CNN can extract the spectral feature information only by using a small amount of training samples effectively.

Key words: hyperspectral remote sensing image classification, deep learning, convolution neural network, spectral redundancy, principal component analysis

中图分类号:

TP391.4

石祥滨, 钟健, 刘翠微, 刘芳, 张德园. 小样本高光谱遥感图像深度学习方法[J]. 系统仿真学报, 2018, 30(7): 2744-2752.

Shi Xiangbin, Zhong Jian, Liu Cuiwei, Liu Fang, Zhang Deyuan. Deep Learning Method for Hyperspectral Remote Sensing Images with Small Samples[J]. Journal of System Simulation, 2018, 30(7): 2744-2752.

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小样本高光谱遥感图像深度学习方法

Deep Learning Method for Hyperspectral Remote Sensing Images with Small Samples

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