3-D Convolution-Recurrent Networks for Spectral-Spatial Classification of Hyperspectral Images

3-D Convolution-Recurrent Networks for Spectral-Spatial Classification of Hyperspectral Images

Nowadays, 3-D convolutional neural networks (3-D CNN) have attracted lots of attention in the spectral-spatial classification of hyperspectral imageries (HSI). In this model, the feed-forward processing structure reduces the computational burden of 3-D structural processing. However, this model as a...

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Bibliographic Details
Main Authors: Majid Seydgar, Amin Alizadeh Naeini, Mengmeng Zhang, Wei Li, Mehran Satari
Format: Article
Language:English
Published: MDPI AG 2019-04-01
Series:Remote Sensing
Subjects:
convolutional neural network (CNN)
recurrent neural network (RNN)
hyperspectral image classification
convolutional long short-term memory (CLSTM)
Online Access:https://www.mdpi.com/2072-4292/11/7/883

Internet

https://www.mdpi.com/2072-4292/11/7/883

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