Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging

Zabalza, Jaime; Ren, Jinchang; Zheng, Jiangbin; Zhao, Huimin; Qing, Chunmei; Yang, Zhijing; Du, Peijun; Marshall, Stephen

doi:10.1016/j.neucom.2015.11.044

Cited by 346 publications

(163 citation statements)

References 27 publications

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“…In [33], Li et al proposed a generalized composite kernel-based method (GCK); first the principal component analysis (PCA) is used to extract the principal components, then the extended multi-attribute morphological profiles (EMAPs) are used to extract spatial information, and lastly the multinomial logistic regression is utilized as the classifier. In addition, the stacked auto-encoder (SAE) is a well-known unsupervised deep feature learning method [21,35]. Considering that the spectrum of the HSI is high and has information redundancy, in order to fully excavate the spectral correlation and reduce the dimensionality, in [21], Zabalza et al segment the spectral band into different groups and then use different SAE networks to extract the deep features.…”

Section: Related Workmentioning

confidence: 99%

“…Inspired by the success of the stacked auto-encoder [21] in unsupervised learning, we propose a novel unsupervised segmented stacked denoising auto-encoder-based feature learning model to extract the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure. Then, a low-rank representation based classification strategy is developed to incorporate both the supervision information from labelled sample and the unsupervised low-rank property among unlabelled samples into a robust classifier.…”

Section: Introductionmentioning

confidence: 99%

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When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

2018

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When confronted with limited labelled samples, most studies adopt an unsupervised feature learning scheme and incorporate the extracted features into a traditional classifier (e.g., support vector machine, SVM) to deal with hyperspectral imagery classification. However, these methods have limitations in generalizing well in challenging cases due to the limited representative capacity of the shallow feature learning model, as well as the insufficient robustness of the classifier which only depends on the supervision of labelled samples. To address these two problems simultaneously, we present an effective low-rank representation-based classification framework for hyperspectral imagery. In particular, a novel unsupervised segmented stacked denoising auto-encoder-based feature learning model is proposed to depict the spatial-spectral characteristics of each pixel in the imagery with deep hierarchical structure. With the extracted features, a low-rank representation based robust classifier is then developed which takes advantage of both the supervision provided by labelled samples and unsupervised correlation (e.g., intra-class similarity and inter-class dissimilarity, etc.) among those unlabelled samples. Both the deep unsupervised feature learning and the robust classifier benefit, improving the classification accuracy with limited labelled samples. Extensive experiments on hyperspectral imagery classification demonstrate the effectiveness of the proposed framework.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

2018

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“…The fundamental unit of SAE named auto-encoder (AE) [45]. It is designed to make the output vector to identical with input vector.…”

Section: Stacked Auto-encodermentioning

confidence: 99%

Flexible Actuator With Variable Stiffness and Its Decoupling Control Algorithm: Principle Prototype Design and Experimental Verification

Jin

Yang²,

Zhang

et al. 2018

IEEE/ASME Trans. Mechatron.

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“…1 for an example) over a video attracted much attention. Recently, owing to the exploration of deep learning [9], [10], the recognition accuracies on these datasets reached almost one hundred percent.…”

Section: Introductionmentioning

confidence: 99%

Unconstrained Face Recognition Using a Set-to-Set Distance Measure on Deep Learned Features

Zhao

Han

Shao

2018

IEEE Trans. Circuits Syst. Video Technol.

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Abstract-Recently considerable efforts have been dedicated to unconstrained face recognition, which requires to identify faces "in the wild" for a set of images and/or video frames captured without human intervention. Unlike traditional face recognition that compares one-to-one media (either a single image or a video frame) only, we encounter a problem of matching sets with heterogeneous contents containing both images and videos. In this paper, we propose a novel Set-to-Set (S2S) distance measure to calculate the similarity between two sets with the aim to improve the recognition accuracy for faces with real-world challenges such as extreme poses or severe illumination conditions. Our S2S distance adopts the kNN-average pooling for the similarity scores computed on all the media in two sets, making the identification far less susceptible to the poor representations (outliers) than traditional feature-average pooling and score-average pooling. Furthermore, we show that various metrics can be embedded into our S2S distance framework, including both predefined and learned ones. This allows to choose the appropriate metric depending on the recognition task in order to achieve the best results. To evaluate the proposed S2S distance, we conduct extensive experiments on the challenging set-based IJB-A face dataset, which demonstrate that our algorithm achieves the stateof-the-art results and is clearly superior to the baselines including several deep learning based face recognition algorithms.

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Novel segmented stacked autoencoder for effective dimensionality reduction and feature extraction in hyperspectral imaging

Cited by 346 publications

References 27 publications

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

When Low Rank Representation Based Hyperspectral Imagery Classification Meets Segmented Stacked Denoising Auto-Encoder Based Spatial-Spectral Feature

Flexible Actuator With Variable Stiffness and Its Decoupling Control Algorithm: Principle Prototype Design and Experimental Verification

Unconstrained Face Recognition Using a Set-to-Set Distance Measure on Deep Learned Features

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