Journal ArticleDOI
Superpixel-based 3D deep neural networks for hyperspectral image classification
Cheng Shi,Chi-Man Pun +1 more
TLDR
A novel hyperspectral image (HSI) classification method to effectively exploit the 3D spectral-spatial information via superpixel-based 3D deep neural networks (3D DNNs).About:
This article is published in Pattern Recognition.The article was published on 2018-02-01. It has received 75 citations till now. The article focuses on the topics: Feature (computer vision).read more
Citations
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Journal ArticleDOI
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.
Journal ArticleDOI
A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network
Kejun Wang,Xiaoxia Qi,Hongda Liu +2 more
TL;DR: The results showed that when the input sequence is increased, the accuracy of the model is improved, and the prediction effect of the hybrid model is the best, followed by that of convolutional neural network.
Journal ArticleDOI
Residual Spectral–Spatial Attention Network for Hyperspectral Image Classification
TL;DR: Zhang et al. as discussed by the authors proposed an end-to-end residual spectral-spatial attention network (RSSAN) for hyperspectral image classification, which takes raw 3D cubes as input data without additional feature engineering.
Journal ArticleDOI
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).
Journal ArticleDOI
Deep Learning Meets Hyperspectral Image Analysis: A Multidisciplinary Review.
TL;DR: The present review is aimed at domain professionals who want to have an updated overview on how hyperspectral acquisition techniques can combine with deep learning architectures to solve specific tasks in different application fields and the machine learning and computer vision experts by giving them a picture of how deep learning technologies are applied to hyperspectrals data from a multidisciplinary perspective.
References
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Journal ArticleDOI
Gradient-based learning applied to document recognition
Yann LeCun,Léon Bottou,Léon Bottou,Yoshua Bengio,Yoshua Bengio,Yoshua Bengio,Patrick Haffner +6 more
TL;DR: In this article, a graph transformer network (GTN) is proposed for handwritten character recognition, which can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters.
Journal ArticleDOI
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
Posted Content
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: Faster R-CNN as discussed by the authors proposes a Region Proposal Network (RPN) to generate high-quality region proposals, which are used by Fast R-NN for detection.
Journal ArticleDOI
A fast learning algorithm for deep belief nets
TL;DR: A fast, greedy algorithm is derived that can learn deep, directed belief networks one layer at a time, provided the top two layers form an undirected associative memory.
Journal ArticleDOI
SLIC Superpixels Compared to State-of-the-Art Superpixel Methods
TL;DR: A new superpixel algorithm is introduced, simple linear iterative clustering (SLIC), which adapts a k-means clustering approach to efficiently generate superpixels and is faster and more memory efficient, improves segmentation performance, and is straightforward to extend to supervoxel generation.
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Yushi Chen,Xing Zhao,Xiuping Jia +2 more