Hyperspectral Image Classification Based on Parameter-Optimized 3D-CNNs Combined with Transfer Learning and Virtual Samples

Hyperspectral Image Classification Based on Parameter-Optimized 3D-CNNs Combined with Transfer Learning and Virtual Samples

Recent research has shown that spatial-spectral information can help to improve the classification of hyperspectral images (HSIs). Therefore, three-dimensional convolutional neural networks (3D-CNNs) have been applied to HSI classification. However, a lack of HSI training samples restricts the perfo...

Full description

Bibliographic Details
Main Authors: Xuefeng Liu, Qiaoqiao Sun, Yue Meng, Min Fu, Salah Bourennane
Format: Article
Language:English
Published: MDPI AG 2018-09-01
Series:Remote Sensing
Subjects:
remote sensing image
convolutional neural network
optimal parameter
lack of sample
tensor analysis
Online Access:http://www.mdpi.com/2072-4292/10/9/1425

Similar Items

Similar Items