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Remote Sens. 2019, 11(7), 883;

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

Department of Geomatics Engineering, Faculty of Civil Engineering and Transportation, University of Isfahan, Isfahan 8174673441, Iran
College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
Author to whom correspondence should be addressed.
Received: 31 January 2019 / Revised: 18 March 2019 / Accepted: 6 April 2019 / Published: 11 April 2019
(This article belongs to the Section Remote Sensing Image Processing)
PDF [11994 KB, uploaded 11 April 2019]


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 vector-based methodology cannot analyze the full content of the HSI information, and as a result, its features are not quite discriminative. On the other hand, convolutional long short-term memory (CLSTM) can recurrently analyze the 3-D structural data to extract more discriminative and abstract features. However, the computational burden of this model as a sequence-based methodology is extremely high. In the meanwhile, the robust spectral-spatial feature extraction with a reasonable computational burden is of great interest in HSI classification. For this purpose, a two-stage method based on the integration of CNN and CLSTM is proposed. In the first stage, 3-D CNN is applied to extract low-dimensional shallow spectral-spatial features from HSI, where information on the spatial features are less than that of the spectral information; consequently, in the second stage, the CLSTM, for the first time, is applied to recurrently analyze the spatial information while considering the spectral one. The experimental results obtained from three widely used HSI datasets indicate that the application of the recurrent analysis for spatial feature extractions makes the proposed model robust against different spatial sizes of the extracted patches. Moreover, applying the 3-D CNN prior to the CLSTM efficiently reduces the model’s computational burden. The experimental results also indicated that the proposed model led to a 1% to 2% improvement compared to its counterpart models. View Full-Text
Keywords: convolutional neural network (CNN); recurrent neural network (RNN); hyperspectral image classification; convolutional long short-term memory (CLSTM) convolutional neural network (CNN); recurrent neural network (RNN); hyperspectral image classification; convolutional long short-term memory (CLSTM)

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Seydgar, M.; Alizadeh Naeini, A.; Zhang, M.; Li, W.; Satari, M. 3-D Convolution-Recurrent Networks for Spectral-Spatial Classification of Hyperspectral Images. Remote Sens. 2019, 11, 883.

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