Deep Recurrent Neural Networks for Hyperspectral Image Classification

By Lichao Mou, Pedram Ghamisi, Xiaoxiang Zhu. IEEE Transactions on Geoscience and Remote Sensing, 2017

1,124 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Media Technology

View Paper: https://www.semanticscholar.org/paper/5a391667242b4a631acdd5917681b16a86523987

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Citations in the corpus

Multiscale Dynamic Graph Convolutional Network for Hyperspectral Image Classification (2019, methodology): “[18] first employed recurrent neural network (RNN) for hyperspectral image classification.”
Deep Learning for Hyperspectral Image Classification: An Overview (2019, methodology): “In contrast to a feedforward neural network, an RNN can recognize patterns in sequences of data and dynamic temporal characteristics by using a recurrent hidden state whose activation at each step depends on that of the previous steps.”
Spectral-Spatial Attention Networks for Hyperspectral Image Classification (2019, methodology): “[13] utilized recurrent neural network (RNN) to model pixel spectra in an HSI as 1D sequence for classification, and they found that the modified gated recurrent unit (GRU) outperforms traditional approaches and the baseline convolutional neural network (CNN).”
Artificial Intelligence and COVID-19: Deep Learning Approaches for Diagnosis and Treatment (2020, background): “dependent on the previous one; hence, the ability of the network to exhibit dynamic temporal behavior [49].”
Building Instance Classification Using Street View Images (2018, background): “Recurrent Neural Network (RNN) is also proved to be efficient for classifying sequence-based data like hyperspectral images (Mou et al., 2017).”
Deep Learning for Classification of Hyperspectral Data: A Comparative Review (2019, methodology): “Except for the RNN from [43] for which our reimplementation underperforms, all of our reimplementations closely approach the results reported in the original papers.”
Federated Learning in Edge Computing: A Systematic Survey (2022, background): “There are different types such as Convolutional Neural Networks (CNNs) [54], Recurrent Neural Networks (RNNs) [55], and Multilayer Perceptrons (MLP) [56], which have already been applied to different themes.”
Deep Learning for Geophysics: Current and Future Trends (2021, methodology): “An alternative means to explore the relationships among different spectrum channels is to use RNN, which regards hyperspectral pixels as sequential data input (Mou et al., 2017).”
Spatial-Spectral Transformer for Hyperspectral Image Classification (2021, methodology): “After that, several deep learning models such as the deep belief network [20], CNN [21,22], recurrent neural network [23,24], generative adversarial network [25,26], and capsule network [27,28] were investigated for HSI classification and obtained good classification performance.”
A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists (2017, methodology): “RNN designs, and then made incremental modifications for efficiency and accuracy gains (Long et al., 2017; Mou et al., 2017; X. X. Zhu et al., 2017).”
Hyperspectral Image Classification—Traditional to Deep Models: A Survey for Future Prospects (2021, methodology): “For the first time, [198] proposed an RNN based HSIC framework with a novel activation function (parametric rectified tanh) and GRU, which utilizes the sequential property of HSI to determine the class labels.”
Deep learning in remote sensing: a review (2017, methodology): “The RNN proposed for the hyperspectral image classification task in [36].”
A survey: Deep learning for hyperspectral image classification with few labeled samples (2021, methodology): “[73] proposed an LSTMbased HSI classification method for the first time, and their work only focused on spectral information.”
A Spectral-Spatial-Dependent Global Learning Framework for Insufficient and Imbalanced Hyperspectral Image Classification (2021, background): “neural networks (RNNs) and convolutional RNNs (CRNNs), which can learn the long short-term spectral dependencies, are widely used in HSI classification [19], [20].”