158 research outputs found
Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
This paper proposes a novel deep learning framework named
bidirectional-convolutional long short term memory (Bi-CLSTM) network to
automatically learn the spectral-spatial feature 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.
Besides, 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 several state-of-the-art methods,
including the CNN framework, on three widely used HSIs. The obtained results
show that Bi-CLSTM can improve the classification performance as compared to
other methods
Hyperspectral image classification using deep convolutional neural networks
The prevailing framework consisted of complex feature extractors following by conventional classifiers. Nevertheless, the high spatial and high spectral dimensionality of each pixel in the hyperspectral imagery hinders the development of hyperspectral image classification. Fortunately, since 2012, deep learning models, which can extract the hierarchical features of large amounts of daily three-channel optical images, have emerged as a better alternative to their shallow learning counterparts. Within all deep learning models, convolutional neural networks (CNNs) exhibit convincing and stunning ability to process a huge mass of data. In this paper, the CNNs have been adopted as an end-to-end pixelwise scheme to classify the pixels of hyperspectral imagery, in which each pixel contains hundreds of continuous spectral bands. According to the preliminarily qualitative and quantitative results, the existing CNN models achieve promising classification accuracy and process effectively and robustly on the University of Pavia dataset
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