R
Renlong Hang
Researcher at Nanjing University of Information Science and Technology
Publications - 55
Citations - 3630
Renlong Hang is an academic researcher from Nanjing University of Information Science and Technology. The author has contributed to research in topics: Hyperspectral imaging & Convolutional neural network. The author has an hindex of 21, co-authored 55 publications receiving 1758 citations. Previous affiliations of Renlong Hang include University of Missouri & Nanjing University.
Papers
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Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
TL;DR: Wang et al. as discussed by the authors proposed a sequence-based recurrent neural network (RNN) for hyperspectral image classification, which makes use of a newly proposed activation function, parametric rectified tanh (PRetanh), instead of the popular tanh or rectified linear unit.
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Feature Extraction for Hyperspectral Imagery: The Evolution From Shallow to Deep: Overview and Toolbox
Behnood Rasti,Danfeng Hong,Renlong Hang,Pedram Ghamisi,Xudong Kang,Jocelyn Chanussot,Jon Atli Benediktsson +6 more
TL;DR: In this article, the curse of dimensionality of hyperspectral images (HSIs) has been discussed, which is a challenge to conventional techniques for accurate analysis of HSIs.
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Spatiotemporal Satellite Image Fusion Using Deep Convolutional Neural Networks
TL;DR: The proposed CNNs-based spatiotemporal fusion method has the following advantages: automatically extracting effective image features; learning an end-to-end mapping between MODIS and LSR Landsat images; and generating more favorable fusion results.
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Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
TL;DR: Wang et al. as mentioned in this paper proposed a cascaded RNN model using gated recurrent units (GRUs) to explore the redundant and complementary information of hyperspectral images (HSIs).
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Bidirectional-Convolutional LSTM Based Spectral-Spatial Feature Learning for Hyperspectral Image Classification
TL;DR: Wang et al. as discussed by the authors proposed a bidirectional-convolutional long short term memory (Bi-CLSTM) network to automatically learn the spectral-spatial features from hyperspectral images (HSIs).