Journal ArticleDOI
Convolutional neural network in network (CNNiN): hyperspectral image classification and dimensionality reduction
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TLDR
This study proposes a novel network pipeline called convolutional neural network in network (which is deeper than the existing approaches) by jointly utilising the spatial and spectral information and produces high-level features from the original HSI.Abstract:
Classification is a principle technique in hyperspectral images (HSIs), where a label is assigned to each pixel based on its characteristics. However, due to lack of labelled training instances in HSIs and also its ultra-high dimensionality, deep learning approaches need a special consideration for HSI classification. As one of the first works in the HSI classification, this study proposes a novel network pipeline called convolutional neural network in network (which is deeper than the existing approaches) by jointly utilising the spatial and spectral information and produces high-level features from the original HSI. This can occur by using spatial-spectral relationships of individual pixel vector at the initial component of the proposed pipeline; the extracted features are then combined to form a joint spatial-spectral feature map. Finally, a recurrent neural network is trained on the extracted features which contain wealthy spectral and spatial properties of the HSI to predict the corresponding label of each vector. The model has been tested on two large scale hyperspectral datasets in terms of classification accuracy, training error, and computational time.read more
Citations
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Journal ArticleDOI
Deep learning classifiers for hyperspectral imaging: A review
TL;DR: A comprehensive review of the current-state-of-the-art in DL for HSI classification, analyzing the strengths and weaknesses of the most widely used classifiers in the literature is provided, providing an exhaustive comparison of the discussed techniques.
Journal ArticleDOI
Extended deep neural network for facial emotion recognition
TL;DR: The aim of this work is to classify each image into one of six facial emotion classes, based on single Deep Convolutional Neural Networks (DNNs), which contain convolution layers and deep residual blocks.
Journal ArticleDOI
CNN-Based Multilayer Spatial–Spectral Feature Fusion and Sample Augmentation With Local and Nonlocal Constraints for Hyperspectral Image Classification
TL;DR: Experimental results on several hyperspectral datasets demonstrate that the proposed CNN method achieves more encouraging classification performance than the current state-of-the-art classification methods, especially with the limited training samples.
Journal ArticleDOI
FuSENet: fused squeeze-and-excitation network for spectral-spatial hyperspectral image classification
TL;DR: The authors propose a bilinear fusion mechanism over different types of squeeze operation such as global pooling and max pooling, which confirms the superiority of the proposed FuSENet method with respect to the state-of-the-art methods.
Journal ArticleDOI
Automatic staging model of heart failure based on deep learning
TL;DR: Convolutional neural network is used as a feature extractor instead of training the entire network to extract the characteristics of the electrocardiogram (ECG) signals and form a feature set to improve the diagnostic accuracy of HF staging.
References
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