A nonlinear and explicit framework of supervised manifold-feature extraction for hyperspectral image classification

Zhang, Peng; He, Honglin; Gao, Lianru

doi:10.1016/j.neucom.2019.01.077

Cited by 29 publications

(8 citation statements)

References 45 publications

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“…(1) Randomly select the pixel point, let the pixel point be the midpoint, establish a window with size n × n, and move the window in different directions [18] (2) Compare the changes of gray value in human multipose motion images. When the window is moved in different directions, when the gray value in the image is fixed, it means that the area is flat [19], and there is no feature point; when moving along a fixed direction and there is only a small change in the gray value, it means that the area is a straight line area…”

Section: Extracting Contour Featurementioning

confidence: 99%

Anomaly Recognition Algorithm for Human Multipose Motion Behavior Using Generative Adversarial Network

Zhang

Ren

et al. 2022

Wireless Communications and Mobile Computing

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Human multipose motion behavior is similar; there are many actions. However, it is difficult to recognize abnormal behavior. The existing human motion behavior anomaly recognition methods have the problems of low accuracy and being time-consuming. Therefore, an anomaly recognition method of human multipose motion behavior using Generative Adversarial Network (GAN) is proposed. The Gauss model is used to segment the human multipose motion behavior image, and the image foreground of the segmentation result is the human motion target detection result. The Shi-Tomasi algorithm is selected to extract contour feature points from human motion object detection results. The extracted contour features are set as hidden space random variables and input into the GAN. The GAN uses the generator and discriminator to recognize the multipose human motion behavior and determine whether the multipose human motion behavior is abnormal. The results show that the proposed algorithm can accurately recognize abnormal human multipose motion behavior, the recognition accuracy is higher than 99%, and the average recognition time is less than 200 ms. The shadow removal effect of the foreground image obtained by the proposed algorithm can realize the accurate recognition of human multipose motion behavior abnormalities and provide a reliable basis for research in related fields.

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Section: Extracting Contour Featurementioning

confidence: 99%

Anomaly Recognition Algorithm for Human Multipose Motion Behavior Using Generative Adversarial Network

Zhang

Ren

et al. 2022

Wireless Communications and Mobile Computing

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“…Hyperspectral data has a specific nonlinear structure in the high dimensional space, and this nonlinear structure is also the area where hyperspectral data are distributed and concentrated in high density [10,15]. The dimensional reduction representation of hyperspectral data can accurately describe the effective information in the data and keep the important information in the data only by maintaining the nonlinear structural relationship in the data [16,17]. Therefore, it is necessary to study the feature representation method that can keep the nonlinear structure relation in hyperspectral data.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint

Ding

Shao

et al. 2021

Electronics

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Due to the high dimensionality and high data redundancy of hyperspectral remote sensing images, it is difficult to maintain the nonlinear structural relationship in the dimensionality reduction representation of hyperspectral data. In this paper, a feature representation method based on high order contractive auto-encoder with nuclear norm constraint (CAE-HNC) is proposed. By introducing Jacobian matrix in the CAE of the nuclear norm constraint, the nuclear norm has better sparsity than the Frobenius norm and can better describe the local low dimension of the data manifold. At the same time, a second-order penalty term is added, which is the Frobenius norm of the Hessian matrix expressed in the hidden layer of the input, encouraging a smoother low-dimensional manifold geometry of the data. The experiment of hyperspectral remote sensing image shows that CAE-HNC proposed in this paper is a compact and robust feature representation method, which provides effective help for the ground object classification and target recognition of hyperspectral remote sensing image.

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“…For supervised FE methods, the new features should contain most discriminative information based on the labeled samples. There exist several supervised FE methods, such as linear discriminant analysis (LDA) [48], nonparametric weighted feature extraction (NWFE) [49], manifold-learning based HSI feature extraction [50], low-rank representation with the ability to preserve the local pairwise constraints information (LRLPC) [51], etc. Supervised methods are usually better than unsupervised methods for HSI classification [52][53][54], since they have access to labeled data.…”

Section: Introductionmentioning

confidence: 99%

Unsupervised and Supervised Feature Extraction Methods for Hyperspectral Images Based on Mixtures of Factor Analyzers

et al. 2020

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This paper proposes three feature extraction (FE) methods based on density estimation for hyperspectral images (HSIs). The methods are a mixture of factor analyzers (MFA), deep MFA (DMFA), and supervised MFA (SMFA). The MFA extends the Gaussian mixture model to allow a low-dimensionality representation of the Gaussians. DMFA is a deep version of MFA and consists of a two-layer MFA, i.e, samples from the posterior distribution at the first layer are input to an MFA model at the second layer. SMFA consists of single-layer MFA and exploits labeled information to extract features of HSI effectively. Based on these three FE methods, the paper also proposes a framework that automatically extracts the most important features for classification from an HSI. The overall accuracy of a classifier is used to automatically choose the optimal number of features and hence performs dimensionality reduction (DR) before HSI classification. The performance of MFA, DMFA, and SMFA FE methods are evaluated and compared to five different types of unsupervised and supervised FE methods by using four real HSIs datasets.

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A nonlinear and explicit framework of supervised manifold-feature extraction for hyperspectral image classification

Cited by 29 publications

References 45 publications

Anomaly Recognition Algorithm for Human Multipose Motion Behavior Using Generative Adversarial Network

Anomaly Recognition Algorithm for Human Multipose Motion Behavior Using Generative Adversarial Network

Hyperspectral Remote Sensing Image Feature Representation Method Based on CAE-H with Nuclear Norm Constraint

Unsupervised and Supervised Feature Extraction Methods for Hyperspectral Images Based on Mixtures of Factor Analyzers

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