Spatial Peak-Aware Collaborative Representation for Hyperspectral Imagery Classification

Zhou, Chengle; Tu, Bing; Ren, Qi; Chen, Siyuan

doi:10.1109/lgrs.2021.3083416

Cited by 13 publications

(4 citation statements)

References 16 publications

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“…To further illustrate the effectiveness of our method, the classification results obtained by several state-of-the-art spec-tral-spatial classification methods, namely EPF [24], IAPS [25], RMGs [26], JSaCR [29], PKCRC [30], SPaCR [31], Multi-scale-CNN [41], 3D CNN [42] and 3D FCN [45] are also reported in this experiment. Besides, the NN-based classification results by the raw HSI data and EMAP which described in Section II have also been taken into account, respectively.…”

Section: Comparison With State-of-the-art Classification Methodsmentioning

confidence: 99%

“…A novel CR-based spatial-spectral approach named probabilistic-kernel collaborative representation classification (PKCRC) which could cover different analysis scenarios by means of a fully adaptive processing chain is proposed in [30] for HSI classification. In addition, Zhou et al [31] proposed a spatial peak-aware collaborative representation (SPaCR) method for HSI classification, which incorporates spectral-spatial information among superpixel clusters into regularization terms to construct a new CR-based closed-form solution.…”

Section: Introductionmentioning

confidence: 99%

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Spectral–Spatial Joint Feature Extraction for Hyperspectral Image Based on High-Reliable Neighborhood Structure

Jia

Zhang

et al. 2021

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

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In recent years, semi-supervised spectral-spatial feature extraction methods for hyperspectral image (HSI) classification have shown promising performance by combining spectral-spatial information and label information. A problem that has not been addressed satisfactorily is that how to effectively and collaboratively use these abundant information contained in the hyperspectral data to exhibit better HSI classification performance. In this paper, a novel feature extraction method based on joint spectral-spatial information is proposed for HSI classification, which consists of the following steps. Firstly, an effective re-expression for the original data is constructed by incorporating texture features extracted by extended multi-attribute profiles (EMAP) with the original HSI. Thus, every pixel can be described by diverse and complementary information in the spectral-spatial domain. Then, the improved neighborhood preserving embedding (NPE) is proposed to establish a relatively accurate reconstruction model and mine high-reliable neighborhood structure from a global perspective by a new distance metric which incorporates spectral bands, texture features and geographical information simultaneously.Finally, the low-dimensional and high-discriminative features for HSI classification are obtained by combining the scatter matrices of local fisher discriminant analysis (LFDA) based on labeled samples and the improved NPE based on the whole data. Experimental results on three real-world HSI data sets show that the proposed method can effectively utilize both the label information and the spectral-spatial information, hence, achieve much better classification performance compared to the conventional FE methods and some state-of-the-art spectral-spatial classification methods.

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Section: Comparison With State-of-the-art Classification Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spectral–Spatial Joint Feature Extraction for Hyperspectral Image Based on High-Reliable Neighborhood Structure

Jia

Zhang

et al. 2021

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

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“…2) Size of Spatial Neighborhood Patch: We evaluate the impact of the spatial neighborhood patch size on the classification accuracy in detail by setting the spatial neighborhood patch λ size range of [17][18][19][20][21][22][23][24][25][26][27][28][29][30][31] with a step size of 2. As shown in Fig.…”

Section: B Parameter Tuningmentioning

confidence: 99%

Hyperspectral Classification via Global-Local Hierarchical Weighting Fusion Network

et al. 2022

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

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The fusion of spectral-spatial features based on deep learning has become the focus of research in hyperspectral image (HSI) classification. However, previous deep frameworks based on spectral-spatial fusion usually performed feature aggregation only at the branch ends. Furthermore, only first-order statistical features are considered in the fusion process, which is not conducive to improving the discrimination of spectral-spatial features. This article proposes a global-local hierarchical weighted fusion endto-end classification architecture. The architecture includes two subnetworks for spectral classification and spatial classification. For the spectral subnetwork, two band-grouping strategies are designed, and bidirectional long short-term memory is used to capture spectral context information from global to local perspectives. For the spatial subnetwork, a pooling strategy based on local attention is combined to construct a global-local pooling fusion module to enhance the discriminability of spatial features learned by a convolutional neural network. For the fusion stage, a hierarchical weighting fusion mechanism is developed to obtain the nonlinear relationship between both spectral and spatial features. The experimental results on four real HSI datasets and a GF-5 satellite dataset demonstrate that the method proposed is more competitive in terms of accuracy and generalization.

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“…Initially, researchers extracted information from a spectral perspective to study HSI classification, and proposed many traditional methods such as K-nearest neighbor (KNN) [16], Bayesian estimation method [17], multinomial logistic regression (MLR) [18], and the support vector machine (SVM) [19]. Furthermore, in order to make full use of spectral features, methods applied to feature selection and feature extraction [20,21] have been proposed, including principal component analysis (PCA) [22], independent component analysis (ICA) [23], and linear discriminant analysis (LDA) [24]. Although these methods perform effectively in mining the potential features of HSI, they easily ignore the correlation of spatial neighborhoods for the spatial structure in HSI.…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Classification with IFormer Network Feature Extraction

Ren

Sha

et al. 2022

Remote Sensing

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Convolutional neural networks (CNNs) are widely used for hyperspectral image (HSI) classification due to their better ability to model the local details of HSI. However, CNNs tends to ignore the global information of HSI, and thus lack the ability to establish remote dependencies, which leads to computational cost consumption and remains challenging. To address this problem, we propose an end-to-end Inception Transformer network (IFormer) that can efficiently generate rich feature maps from HSI data and extract high- and low-frequency information from the feature maps. First, spectral features are extracted using batch normalization (BN) and 1D-CNN, while the Ghost Module generates more feature maps via low-cost operations to fully exploit the intrinsic information in HSI features, thus improving the computational speed. Second, the feature maps are transferred to Inception Transformer through a channel splitting mechanism, which effectively learns the combined features of high- and low-frequency information in the feature maps and allows for the flexible modeling of discriminative information scattered in different frequency ranges. Finally, the HSI features are classified via pooling and linear layers. The IFormer algorithm is compared with other mainstream algorithms in experiments on four publicly available hyperspectral datasets, and the results demonstrate that the proposed method algorithm is significantly competitive among the HSI classification algorithms.

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Spatial Peak-Aware Collaborative Representation for Hyperspectral Imagery Classification

Cited by 13 publications

References 16 publications

Spectral–Spatial Joint Feature Extraction for Hyperspectral Image Based on High-Reliable Neighborhood Structure

Spectral–Spatial Joint Feature Extraction for Hyperspectral Image Based on High-Reliable Neighborhood Structure

Hyperspectral Classification via Global-Local Hierarchical Weighting Fusion Network

Hyperspectral Image Classification with IFormer Network Feature Extraction

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