Deep Belief Network for Spectral–Spatial Classification of Hyperspectral Remote Sensor Data

Sensors

卷:19

Yao Yang¹  Chenming Li¹  Yongchang Wang¹  Hongmin Gao¹  Jiawei Wang¹  Xiaoke Zhang² 

[1] College of Computer and Information, Hohai University, Nanjing 211100, China;

[2] School of Public Administration, Hohai University, Nanjing 211100, China;

关键词: hyperspectral image;    deep learning;    feature extraction;    classification;    remote sensors;    multi-sensor fusion;   

DOI  :  10.3390/s19010204

来源: DOAJ

【 摘 要 】

With the development of high-resolution optical sensors, the classification of ground objects combined with multivariate optical sensors is a hot topic at present. Deep learning methods, such as convolutional neural networks, are applied to feature extraction and classification. In this work, a novel deep belief network (DBN) hyperspectral image classification method based on multivariate optical sensors and stacked by restricted Boltzmann machines is proposed. We introduced the DBN framework to classify spatial hyperspectral sensor data on the basis of DBN. Then, the improved method (combination of spectral and spatial information) was verified. After unsupervised pretraining and supervised fine-tuning, the DBN model could successfully learn features. Additionally, we added a logistic regression layer that could classify the hyperspectral images. Moreover, the proposed training method, which fuses spectral and spatial information, was tested over the Indian Pines and Pavia University datasets. The advantages of this method over traditional methods are as follows: (1) the network has deep structure and the ability of feature extraction is stronger than traditional classifiers; (2) experimental results indicate that our method outperforms traditional classification and other deep learning approaches.

【 授权许可】

Unknown