Discriminant collaborative neighborhood preserving embedding for hyperspectral imagery

Liu, Meng; Zhao, Xianfeng; Liu, Liming; Jing, Ling

doi:10.1117/1.jrs.11.046004

Cited by 8 publications

(5 citation statements)

References 27 publications

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“…In order to evaluate the performance of different algorithms, the experiment was repeated 20 times with randomly selected training sets, and finally the average classification accuracy of each method was obtained. DCNPE method in literature [Lv et al (2017)], LADA method in literature [Wang et al (2017)], DLPP method in literature [Deng et al (2015)], Kernel Principal Component Analysis (KPCA for short), sparse preserving projection (SPP for short) and domain-preserving embedding (NPE for short) are selected and compared with MDASSP method. For better comparison, the parameters of each method are adjusted to the best.…”

Section: Resultsmentioning

confidence: 99%

“…Based on existing research [Uddin et al (2019), Jayaprakash et al (2020), Wan et al (2017, Dongyang andLi (2018), Qiao et (2010), Kianisarkaleh and Ghassemian (2016), Zhai et al (2016), Gao et al (2016), Wang et al (2017), Lv et al (2017), Tabejamaat and Mousavi (2017), Dong et al (2021), Yc (2021), Yuan (2021)], this paper proposes a hyperspectral image classification method based on Manifold Data Analysis and Sparse Subspace Projection (MDASSP). Good results have been achieved in small samples.…”

Section: Introdutionmentioning

confidence: 99%

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Hyperspectral Image Classification Based on Manifold Data Analysis and Sparse Subspace Projection

Zheng

Peng

2021

Int. J. Eng. Tech. Mgmt. Res.

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Aiming at the problem of "dimension disaster" in hyperspectral image classification, a method of dimension reduction based on manifold data analysis and sparse subspace projection (MDASSP) is proposed. The sparse coefficient matrix is established by the new method, and the sparse subspace projection is carried out by the optimization method. To keep the geometric structure of the manifold, the objective function is regularized by the manifold learning method. The new method combines sparse coding and manifold learning to generate features with better classification ability. The experimental results show that the new method is better than other methods in the case of small samples.

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Section: Resultsmentioning

confidence: 99%

Section: Introdutionmentioning

confidence: 99%

Hyperspectral Image Classification Based on Manifold Data Analysis and Sparse Subspace Projection

Zheng

Peng

2021

Int. J. Eng. Tech. Mgmt. Res.

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“…Manifold learning methods attempt to reveal the data that lie on or near a manifold in the original data space, which are expected to be highly useful for feature extraction of hyperspectral image. Locality preserving projection [15] and neighborhood preserving embedding (NPE) [16] are two classical linear manifold learning methods, and they provide good performance for classification.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Residual Convolutional Neural Network for Hyperspectral Image Classification

Hong

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this article, we designed an adaptive residual convolutional neural network (ARCNN) that takes raw hyperspectral image (HSI) cubes as input data for land-cover classification. In this network, spectral and spatial feature extraction blocks are explored to learn discriminative features from abundant spectral information and spatial contexts in HSIs. The proposed ARCNN is an end-to-end deep learning framework that alleviates the decliningaccuracy phenomenon of deep learning models, and it also ranks the correlation and importance of each band in HSIs. Furthermore, the residual blocks connect every other 3-D convolutional layer by using an identity mapping, which facilitates backpropagation of gradients. In order to address the common issue of imbalance between high dimensionality and limited availability of training samples for HSI classification, an attention mechanism and a feature fusion block are investigated to improve the performance of the ARCNN. Finally, some strategies, batch normalization and dropout, are imposed on every convolutional layer to regularize the learning process. Therefore, the ARCNN method brings benefits to extract discriminative features, and it is easier to avoid overfitting. Experimental results on three public HSI datasets demonstrate the effectiveness of the ARCNN over some state-of-the-art methods.

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“…Principal component analysis (PCA) [19] and linear discriminant analysis (LDA) [20] are two typical subspace linear transformation approaches, but they cannot effectively reveal the nonlinear structure of the data. For this reason, researchers put forward some manifold learning methods, which can better mine potential low-dimensional manifold structures of high-dimensional data, such as local preserving projection (LPP) [21], locally linear embedding [22] and neighborhood preserving embedding [23]. The above methods can be classified into graph embedding framework.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Spectral Joint Classification of Hyperspectral Image With Locality and Edge Preserving

Zhang

Liu

Huanhuan

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Hyperspectral image (HSI) classification is an important part of its processing and application. Aiming at the problems of high data dimensionality and high spatial neighborhood correlation in HSI classification, we propose a spatial-spectral joint classification method of HSI with locality and edge preserving in this article. First, the input HSI is normalized, and the feature is extracted by principal component analysis. The first principal component image is taken as the guidance image. Second, guided filtering is used to extract the spatial features of each band separately. Then, the extracted spatial features are superimposed, and low-dimensional embedding is completed through local Fisher discriminant analysis. Finally, the obtained low-dimensional embedded features are input into a random forest classifier to get classification results. The experimental results of two HSI show that the proposed method achieves higher classification accuracy than other related methods. In the case of randomly selecting 10% and 1% samples from each class of ground object as training samples, the overall classification accuracy is improved to 99.57% and 97.79%, respectively. This method effectively uses the spatial and local information of the image in low dimensional embedding, and preserves the boundaries of the ground objects, thus improving the classification effect.

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Discriminant collaborative neighborhood preserving embedding for hyperspectral imagery

Cited by 8 publications

References 27 publications

Hyperspectral Image Classification Based on Manifold Data Analysis and Sparse Subspace Projection

Hyperspectral Image Classification Based on Manifold Data Analysis and Sparse Subspace Projection

Adaptive Residual Convolutional Neural Network for Hyperspectral Image Classification

Spatial-Spectral Joint Classification of Hyperspectral Image With Locality and Edge Preserving

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