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Abstract
Deep neural networks, such as convolutional neural networks (CNN) and stacked autoencoders, have recently been successfully used to extract deep features for hyperspectral data classification. Recurrent neural networks (RNN) are another type of neural networks, which are widely used for sequence analysis because they are constructed to extract contextual information from sequences by modeling the dependencies between different time steps. In this paper, we study the ability of RNN for hyperspectral data classification by extracting the contextual information from the data. Specifically, hyperspectral data are treated as spectral sequences, and an RNN is used to model the dependencies between different spectral bands. In addition, we propose to use a convolutional recurrent neural network (CRNN) to learn more discriminative features for hyperspectral data classification. In CRNN, a few convolutional layers are first learned to extract middle-level and locally-invariant features from the input data, and the following recurrent layers are then employed to further extract spectrally-contextual information from the features generated by the convolutional layers. Experimental results on real hyperspectral datasets show that our method provides better classification performance compared to traditional methods and other state-of-the-art deep learning methods for hyperspectral data classification.
Highlights
In the last decade, with the advances of the computing power of computers and the availability of large-scale datasets, deep learning [1] techniques, such as deep belief networks (DBN), deep convolutional neural networks (CNN) and deep recurrent neural networks (RNN), have gained great success in a variety of machine learning tasks, such as computer vision, speech recognition, natural language processing, etc
The remainder of this paper is organized as follows: Section 2 talks about different deep neural networks for hyperspectral data classification: we provide a basic overview for CNNs and how they have been used for hyperspectral data classification, introduce RNNs for hyperspectral data classification and describe how to combine CNN with RNN to get the proposed method convolutional recurrent neural network (CRNN) for hyperspectral data classification
The fact that the performance of CRNN/CLSTM are significantly better than RNN/long short-term memory (LSTM) tells us that the middle-level features extracted by the convolutional layers in CRNN/CLSTM help the following recurrent layers to better capture the contextual information
Summary
With the advances of the computing power of computers and the availability of large-scale datasets, deep learning [1] techniques, such as deep belief networks (DBN), deep convolutional neural networks (CNN) and deep recurrent neural networks (RNN), have gained great success in a variety of machine learning tasks, such as computer vision, speech recognition, natural language processing, etc. Network is a special type of RNN, which is able to capture very long-term dependencies embedded in sequence data. Both the regular RNN and the LSTM networks have been successfully used for time series data analysis, such as speech recognition [12,13,14,15], machine translation [16,17,18], etc. Deep CNN is usually used when the network has Remote Sens. 2017, 9, 298; doi:10.3390/rs9030298 www.mdpi.com/journal/remotesensing
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