Abstract
This paper proposes a novel deep learning framework named bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial features from hyperspectral images (HSIs). In the network, the issue of spectral feature extraction is considered as a sequence learning problem, and a recurrent connection operator across the spectral domain is used to address it. Meanwhile, inspired from the widely used convolutional neural network (CNN), a convolution operator across the spatial domain is incorporated into the network to extract the spatial feature. In addition, to sufficiently capture the spectral information, a bidirectional recurrent connection is proposed. In the classification phase, the learned features are concatenated into a vector and fed to a Softmax classifier via a fully-connected operator. To validate the effectiveness of the proposed Bi-CLSTM framework, we compare it with six state-of-the-art methods, including the popular 3D-CNN model, on three widely used HSIs (i.e., Indian Pines, Pavia University, and Kennedy Space Center). The obtained results show that Bi-CLSTM can improve the classification performance by almost 1.5 % as compared to 3D-CNN.
Highlights
Current hyperspectral sensors can acquire images with high spectral and spatial resolutions simultaneously
Similar to convolutional neural network (CNN), the convolution operation are followed by max-pooling in Bi-convolutional LSTM (CLSTM), and we empirically set the size of convolution kernel to 3 × 3 and the number of convolution kernel to 32
We propose a novel bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial feature from hyperspectral images (HSIs)
Summary
Current hyperspectral sensors can acquire images with high spectral and spatial resolutions simultaneously. Convolutional neural network (CNN) based deep models have been popularly used [2,34] They directly take the original image or the local image patch as network inputs, and use local-connected and weight sharing structure to extract the spatial features from HSIs. In [2], the authors designed a CNN network with three convolutional layers and one fully-connected layer. In order to extract the spectral-spatial features from HSIs, the authors consider the 3D image patches as the input of the network This complex structure will inevitably increase the amount of parameters, leading to the overfitting problem with a limited number of training samples. We consider images in all the spectral bands as an image sequence, and use LSTM to effectively model their relationships; second, considering the specific characteristics of hyperspectral images, we further propose a unified framework to combine the merits of LSTM and CNN for spectral-spatial feature extraction
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