Multi-Feature Manifold Discriminant Analysis for Hyperspectral Image Classification

Hyperspectral image (HSI) provides both spatial structure and spectral information for classification, but many traditional methods simply concatenate spatial features and spectral features together that usually lead to the curse-of-dimensionality and unbalanced representation of different features. To address this issue, a new dimensionality reduction (DR) method, termed multi-feature manifold discriminant analysis (MFMDA), was proposed in this paper. At first, MFMDA explores local binary patterns (LBP) operator to extract textural features for encoding the spatial information in HSI. Then, under graph embedding framework, the intrinsic and penalty graphs of LBP and spectral features are constructed to explore the discriminant manifold structure in both spatial and spectral domains, respectively. After that, a new spatial-spectral DR model for multi-feature fusion is built to extract discriminant spatial-spectral combined features, and it not only preserves the similarity relationship between spectral features and LBP features but also possesses strong discriminating ability in the low-dimensional embedding space. Experiments on Indian Pines, Heihe and Pavia University (PaviaU) hyperspectral data sets demonstrate that the proposed MFMDA method performs significantly better than some state-of-the-art methods using only single feature or simply stacking spectral features and spatial features together, and the classification accuracies of it can reach 95.43%, 97.19% and 96.60%, respectively.

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Hyperspectral Image Classification Based on Mathematical Morphology and Tensor Decomposition

Mathematical Morphology - Theory and Applications ◽

10.1515/mathm-2020-0001 ◽

2020 ◽

Vol 4 (1) ◽

pp. 1-30

Author(s):

Mohamad Jouni ◽

Mauro Dalla Mura ◽

Pierre Comon

Keyword(s):

Mathematical Morphology ◽

Spatial Information ◽

Hyperspectral Image ◽

Feature Space ◽

Tensor Decomposition ◽

Hyperspectral Data ◽

Spatial Features ◽

Cp Decomposition ◽

Decomposition Experiments ◽

Low Dimensional

AbstractHyperspectral Image (HSI) classification refers to classifying hyperspectral data into features, where labels are given to pixels sharing the same features, distinguishing the present materials of the scene from one another. Naturally a HSI acquires spectral features of pixels, but spatial features based on neighborhood information are also important, which results in the problem of spectral-spatial classification. There are various ways to account to spatial information, one of which is through Mathematical Morphology, which is explored in this work. A HSI is a third-order data block, and building new spatial diversities may increase this order. In many cases, since pixel-wise classification requires a matrix of pixels and features, HSI data are reshaped as matrices which causes high dimensionality and ignores the multi-modal structure of the features. This work deals with HSI classification by modeling the data as tensors of high order. More precisely, multi-modal hyperspectral data is built and dealt with using tensor Canonical Polyadic (CP) decomposition. Experiments on real HSI show the effectiveness of the CP decomposition as a candidate for classification thanks to its properties of representing the pixel data in a matrix compact form with a low dimensional feature space while maintaining the multi-modality of the data.

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Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification

Remote Sensing ◽

10.3390/rs12020280 ◽

2020 ◽

Vol 12 (2) ◽

pp. 280 ◽

Cited By ~ 3

Author(s):

Liqin Liu ◽

Zhenwei Shi ◽

Bin Pan ◽

Ning Zhang ◽

Huanlin Luo ◽

...

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Hyperspectral Image ◽

Feature Fusion ◽

Hyperspectral Data ◽

Learning Technology ◽

Learning Networks ◽

Spatial Features ◽

Training Samples ◽

Rgb Image

In recent years, deep learning technology has been widely used in the field of hyperspectral image classification and achieved good performance. However, deep learning networks need a large amount of training samples, which conflicts with the limited labeled samples of hyperspectral images. Traditional deep networks usually construct each pixel as a subject, ignoring the integrity of the hyperspectral data and the methods based on feature extraction are likely to lose the edge information which plays a crucial role in the pixel-level classification. To overcome the limit of annotation samples, we propose a new three-channel image build method (virtual RGB image) by which the trained networks on natural images are used to extract the spatial features. Through the trained network, the hyperspectral data are disposed as a whole. Meanwhile, we propose a multiscale feature fusion method to combine both the detailed and semantic characteristics, thus promoting the accuracy of classification. Experiments show that the proposed method can achieve ideal results better than the state-of-art methods. In addition, the virtual RGB image can be extended to other hyperspectral processing methods that need to use three-channel images.

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A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs12010125 ◽

2020 ◽

Vol 12 (1) ◽

pp. 125 ◽

Cited By ~ 5

Author(s):

Mu ◽

Guo ◽

Liu

Keyword(s):

Neural Network ◽

Hyperspectral Image ◽

Feature Fusion ◽

Hyperspectral Images ◽

Spectral Features ◽

Hyperspectral Image Classification ◽

Spatial Feature ◽

Multi Scale ◽

Spatial Features ◽

Multi Level

Extracting spatial and spectral features through deep neural networks has become an effective means of classification of hyperspectral images. However, most networks rarely consider the extraction of multi-scale spatial features and cannot fully integrate spatial and spectral features. In order to solve these problems, this paper proposes a multi-scale and multi-level spectral-spatial feature fusion network (MSSN) for hyperspectral image classification. The network uses the original 3D cube as input data and does not need to use feature engineering. In the MSSN, using different scale neighborhood blocks as the input of the network, the spectral-spatial features of different scales can be effectively extracted. The proposed 3D–2D alternating residual block combines the spectral features extracted by the three-dimensional convolutional neural network (3D-CNN) with the spatial features extracted by the two-dimensional convolutional neural network (2D-CNN). It not only achieves the fusion of spectral features and spatial features but also achieves the fusion of high-level features and low-level features. Experimental results on four hyperspectral datasets show that this method is superior to several state-of-the-art classification methods for hyperspectral images.

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Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs

Remote Sensing ◽

10.3390/rs10081271 ◽

2018 ◽

Vol 10 (8) ◽

pp. 1271 ◽

Cited By ~ 14

Author(s):

Feng Gao ◽

Qun Wang ◽

Junyu Dong ◽

Qizhi Xu

Keyword(s):

Image Classification ◽

Spatial Information ◽

Hyperspectral Image ◽

Small Sample ◽

Hyperspectral Data ◽

Hyperspectral Images ◽

High Dimensional ◽

Hyperspectral Image Classification ◽

Spatial Classification ◽

Spatial Features

Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods.

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HYPERSPECTRAL IMAGE CLASSIFICATION USING RESIDUAL 2D AND 3D CONVOLUTIONAL NEURAL NETWORK JOINT ATTENTION MODEL

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliv-m-3-2021-187-2021 ◽

2021 ◽

Vol XLIV-M-3-2021 ◽

pp. 187-193

Author(s):

Q. Yuan ◽

Y. Ang ◽

H. Z. M. Shafri

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Classification Accuracy ◽

Spatial Information ◽

Hyperspectral Image ◽

Spectral Features ◽

Hyperspectral Image Classification ◽

Spatial Features ◽

3D Cnn ◽

2D And 3D

Abstract. Hyperspectral image classification (HSIC) is a challenging task in remote sensing data analysis, which has been applied in many domains for better identification and inspection of the earth surface by extracting spectral and spatial information. The combination of abundant spectral features and accurate spatial information can improve classification accuracy. However, many traditional methods are based on handcrafted features, which brings difficulties for multi-classification tasks due to spectral intra-class heterogeneity and similarity of inter-class. The deep learning algorithm, especially the convolutional neural network (CNN), has been perceived promising feature extractor and classification for processing hyperspectral remote sensing images. Although 2D CNN can extract spatial features, the specific spectral properties are not used effectively. While 3D CNN has the capability for them, but the computational burden increases as stacking layers. To address these issues, we propose a novel HSIC framework based on the residual CNN network by integrating the advantage of 2D and 3D CNN. First, 3D convolutions focus on extracting spectral features with feature recalibration and refinement by channel attention mechanism. The 2D depth-wise separable convolution approach with different size kernels concentrates on obtaining multi-scale spatial features and reducing model parameters. Furthermore, the residual structure optimizes the back-propagation for network training. The results and analysis of extensive HSIC experiments show that the proposed residual 2D-3D CNN network can effectively extract spectral and spatial features and improve classification accuracy.

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Spectral-Spatial Hyperspectral Image Semisupervised Classification by Fusing Maximum Noise Fraction and Adaptive Random Multigraphs

Discrete Dynamics in Nature and Society ◽

10.1155/2021/9998185 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Eryang Chen ◽

Ruichun Chang ◽

Kaibo Shi ◽

Ansheng Ye ◽

Fang Miao ◽

...

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

Hyperspectral Data ◽

Spatial Features ◽

Training Samples ◽

Semisupervised Classification ◽

Ground Object ◽

Multiscale Local Binary Pattern ◽

Noise Fraction ◽

Reduced Data

Hyperspectral images (HSIs) contain large amounts of spectral and spatial information, and this provides the possibility for ground object classification. However, when using the traditional method, achieving a satisfactory classification result is difficult because of the insufficient labeling of samples in the training set. In addition, parameter adjustment during HSI classification is time-consuming. This paper proposes a novel fusion method based on the maximum noise fraction (MNF) and adaptive random multigraphs for HSI classification. Considering the overall spectrum of the object and the correlation of adjacent bands, the MNF was utilized to reduce the spectral dimension. Next, a multiscale local binary pattern (LBP) analysis was performed on the MNF dimension-reduced data to extract the spatial features of different scales. The obtained multiscale spatial features were then stacked with the MNF dimension-reduced spectral features to form multiscale spectral-spatial features (SSFs), which were sent into the RMG for HSI classification. Optimal performance was obtained by fusion. For all three real datasets, our method achieved competitive results with only 10 training samples. More importantly, the classification parameters corresponding to different hyperspectral data can be automatically optimized using our method.

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Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs13173396 ◽

2021 ◽

Vol 13 (17) ◽

pp. 3396

Author(s):

Feng Zhao ◽

Junjie Zhang ◽

Zhe Meng ◽

Hanqiang Liu

Keyword(s):

Receptive Field ◽

Spatial Information ◽

Hyperspectral Image ◽

Feature Fusion ◽

Receptive Fields ◽

Classification Performance ◽

Convolutional Network ◽

Dilated Convolution ◽

Spatial Features ◽

Good Classification Performance

Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convolutional network (PDCNet) is proposed in this paper. Firstly, a pyramidal dilated convolutional (PDC) layer integrates different numbers of sub-dilated convolutional layers is proposed, where the dilated factor of the sub-dilated convolution increases exponentially, achieving multi-sacle receptive fields. Secondly, the number of sub-dilated convolutional layers increases in a pyramidal pattern with the depth of the network, thereby capturing more comprehensive hyperspectral information in the receptive field. Furthermore, a feature fusion mechanism combining pixel-by-pixel addition and channel stacking is adopted to extract more abstract spectral–spatial features. Finally, in order to reuse the features of the previous layers more effectively, dense connections are applied in densely pyramidal dilated convolutional (DPDC) blocks. Experiments on three well-known HSI datasets indicate that PDCNet proposed in this paper has good classification performance compared with other popular models.

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Spectral-Spatial Joint Classification of Hyperspectral Image Based on Broad Learning System

Remote Sensing ◽

10.3390/rs13040583 ◽

2021 ◽

Vol 13 (4) ◽

pp. 583

Author(s):

Guixin Zhao ◽

Xuesong Wang ◽

Yi Kong ◽

Yuhu Cheng

Keyword(s):

Classification Accuracy ◽

Spatial Information ◽

Hyperspectral Image ◽

Learning System ◽

Classification Model ◽

Spectral Features ◽

Gaussian Filter ◽

Spatial Features ◽

Joint Classification

At present many researchers pay attention to a combination of spectral features and spatial features to enhance hyperspectral image (HSI) classification accuracy. However, the spatial features in some methods are utilized insufficiently. In order to further improve the performance of HSI classification, the spectral-spatial joint classification of HSI based on the broad learning system (BLS) (SSBLS) method was proposed in this paper; it consists of three parts. Firstly, the Gaussian filter is adopted to smooth each band of the original spectra based on the spatial information to remove the noise. Secondly, the test sample’s labels can be obtained using the optimal BLS classification model trained with the spectral features smoothed by the Gaussian filter. At last, the guided filter is performed to correct the BLS classification results based on the spatial contextual information for improving the classification accuracy. Experiment results on the three real HSI datasets demonstrate that the mean overall accuracies (OAs) of ten experiments are 99.83% on the Indian Pines dataset, 99.96% on the Salinas dataset, and 99.49% on the Pavia University dataset. Compared with other methods, the proposed method in the paper has the best performance.

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Cluster-Wise Weighted NMF for Hyperspectral Images Unmixing with Imbalanced Data

Remote Sensing ◽

10.3390/rs13020268 ◽

2021 ◽

Vol 13 (2) ◽

pp. 268

Author(s):

Xiaochen Lv ◽

Wenhong Wang ◽

Hongfu Liu

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

Imbalanced Data ◽

Reconstruction Error ◽

Hyperspectral Data ◽

Weight Matrix ◽

Hyperspectral Images ◽

Mixed Data ◽

Sparsity Constraints ◽

Additional Constraints

Hyperspectral unmixing is an important technique for analyzing remote sensing images which aims to obtain a collection of endmembers and their corresponding abundances. In recent years, non-negative matrix factorization (NMF) has received extensive attention due to its good adaptability for mixed data with different degrees. The majority of existing NMF-based unmixing methods are developed by incorporating additional constraints into the standard NMF based on the spectral and spatial information of hyperspectral images. However, they neglect to exploit the nature of imbalanced pixels included in the data, which may cause the pixels mixed with imbalanced endmembers to be ignored, and thus the imbalanced endmembers generally cannot be accurately estimated due to the statistical property of NMF. To exploit the information of imbalanced samples in hyperspectral data during the unmixing procedure, in this paper, a cluster-wise weighted NMF (CW-NMF) method for the unmixing of hyperspectral images with imbalanced data is proposed. Specifically, based on the result of clustering conducted on the hyperspectral image, we construct a weight matrix and introduce it into the model of standard NMF. The proposed weight matrix can provide an appropriate weight value to the reconstruction error between each original pixel and the reconstructed pixel in the unmixing procedure. In this way, the adverse effect of imbalanced samples on the statistical accuracy of NMF is expected to be reduced by assigning larger weight values to the pixels concerning imbalanced endmembers and giving smaller weight values to the pixels mixed by majority endmembers. Besides, we extend the proposed CW-NMF by introducing the sparsity constraints of abundance and graph-based regularization, respectively. The experimental results on both synthetic and real hyperspectral data have been reported, and the effectiveness of our proposed methods has been demonstrated by comparing them with several state-of-the-art methods.

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