基于深度学习的高光谱图像空-谱联合特征提取

黄鸿; 何凯; 郑新磊; 石光耀

doi:doi:10.3788/lop54.101001

激光与光电子学进展, 2017, 54 (10): 101001, 网络出版: 2017-10-09

基于深度学习的高光谱图像空-谱联合特征提取下载： 1267次

Spatial-Spectral Feature Extraction of Hyperspectral Image Based on Deep Learning

论文大纲

黄鸿何凯郑新磊石光耀

作者单位

重庆大学光电技术及系统教育部重点实验室, 重庆 400044

图像处理高光谱图像分类特征提取深度学习空间信息 image processing hyperspectral image classification feature extraction deep learning spatial information

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摘要

由于高光谱遥感数据具有波段多、特征非线性、空间相关等特点, 提出一种基于深度学习的空-谱联合(SSDL)特征提取算法来有效提取数据中的空-谱特征。该算法利用多层深度学习模型--堆栈自动编码机对高光谱数据进行逐层学习, 挖掘图像中的深层非线性特征, 然后再根据每个特征像元的空间近邻信息, 对样本深度特征和空间信息进行空-谱联合, 增加同类数据聚集性和非同类数据分散度, 提升后续分类性能。在帕维亚大学和萨利纳斯山谷高光谱数据集上进行地物分类实验: 在1%样本比例下, 地物总体分类精度达到了91.05%和94.16%; 在5%样本比例下, 地物总体分类精度达到了97.38%和97. 50%。结果表明: 由于SSDL特征提取算法融合了数据中深层非线性特征和空间信息, 能够提取出更具鉴别特性的特征, 较其他同类算法能够获取更高分类精度。

Abstract

On the basis of the characteristics of multi-band, nonlinear and spatial correlation of hyperspectral remote sensing data, a new feature extraction algorithm based on spatial-spectral deep learning (SSDL) is proposed. This algorithm uses a multiple layers deep learning model, which is the stacked automatic encoder to study high spectral data layer by layer and explore the deep nonlinear characteristics of the image. Based on the spatial neighbor information of each feature pixel, the spatial-spectral combination of sample depth feature and spatial information is used to increase the compactness of homogeneous data and the separability of non-homogeneous data, and improve the performance of subsequent classification. The ground objects classification experiments are performed on Pavia University and Salinas Valley hyperspectral remote sensing datasets. When sample proportion is 1%, the ground objects overall classification accuracy reaches 91.05% and 94.16%. When sample proportion is 5%, the ground objects overall classification accuracy reaches 97.38% and 97.50%. The results show that the SSDL feature extraction algorithm fuses the deep nonlinear characteristics and spatial information of data. It can effectively extract the discriminant features, and obtain higher classification accuracy than other algorithms.

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黄鸿, 何凯, 郑新磊, 石光耀. 基于深度学习的高光谱图像空-谱联合特征提取[J]. 激光与光电子学进展, 2017, 54(10): 101001. Huang Hong, He Kai, Zheng Xinlei, Shi Guangyao. Spatial-Spectral Feature Extraction of Hyperspectral Image Based on Deep Learning[J]. Laser & Optoelectronics Progress, 2017, 54(10): 101001.

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