Local-View-Assisted Discriminative Band Selection With Hypergraph Autolearning for Hyperspectral Image Classification

Wei, Xiaohui; Cai, Liang; Liao, Bo; Lu, Ting

doi:10.1109/tgrs.2019.2952383

Cited by 17 publications

(9 citation statements)

References 59 publications

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“…MSFGW produces a larger improvement in results compared to BSNet-Conv [27], which also indicates that the combination of spatial and spectral information better characterizes the internal structure of hyperspectral responsibility. The results of LvaHAI [39] are similar to BSNet-Conv [27], but the optimal results appear earlier as the number of choices increases. The other algorithms have poorer classification results with large fluctuations on this dataset, so the algorithms are not as robust.…”

Section: ) Classification Performance With Different Numbers Of Selec...mentioning

confidence: 67%

“…In addition to qualitative analysis, three popular quantitative analysis standards, overall accuracy (OA), average accuracy (AA), and Kappa coefficient (Kappa), are also used as experimental evaluation indicators. To illustrate the advance of the proposed algorithm, it compared with state-of-the-art band selection methods such as LvaHAI [39], EGCSR_BS [40], IBRA-GSS [41], and NGNMF-E2DSSA [42]. Furthermore, comparative experiments of all bands are added to intuitively analyze the performance.…”

Section: Resultsmentioning

confidence: 99%

“…It can be seen that MSFGW achieves the best OA(%), AA(%), and Kappa. When greater than 15, the classification accuracy of LvaHAI [39] is higher than that of BSNet-Conv [27]. The results show that the graph learning algorithm has advantages in mining HSI band clustering structure over using spectral information alone.…”

Section: ) Classification Performance With Different Numbers Ofmentioning

confidence: 93%

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Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection

Wang

Liu

Cui

et al. 2023

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

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Feature selection is an important way to achieve high precision and efficient classification of hyperspectral remote sensing images. However, most existing FS methods use a fixed scale to extract features and the relationship between spatial and spectral dimensions is ignored. In fact, this correlation is useful for classification. In this paper, a multi-scale feature fusion network (MSFGW) is proposed in which a global weighting mechanism is explored to catch spatial-spectral information at multiple scales. First, the multi-scale feature extraction module composed of group convolution and dilated convolution is utilized to extract the multiscale features. With the increase of the dilation rate, the module takes the spatial differences at varying scales. Secondly, a 3D weighting mechanism is used to combine the spatial and spectral correlated information for reducing the interference of homologous and heterologous and boosting the feature discrimination ability. Then, multi-scale weighted features are fused to integrate the internal information of all bands at different scales. Finally, the band reconstruction network is used to select representative bands according to their entropy. The experimental results with the state-of-the-art feature selection algorithms on three widely hyperspectral datasets demonstrate that the features selected by MSFGW have obvious advantages in classification with only a few training samples.

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Section: ) Classification Performance With Different Numbers Of Selec...mentioning

confidence: 67%

Section: Resultsmentioning

confidence: 99%

Section: ) Classification Performance With Different Numbers Ofmentioning

confidence: 93%

See 1 more Smart Citation

Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection

Wang

Liu

Cui

et al. 2023

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

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show abstract

“…Duan et al [29] present a local constraint-based sparse manifold hypergraph learning algorithm to discover the manifold-based light structure and the multivariate discriminant sparse relationship of hyperspectral images. Wei et al [30] introduce an information-sharing mechanism to share the same structural distribution while preserving the specificity of each low-dimensional representation via adjusting the view-dependent hyperedge weights. To reduce the dimension of the hyperspectral image, Luo et al [31] propose a sparse-adaptive hypergraph discriminant analysis method for adaptively revealing the intrinsic structure relationships with sparse representation.…”

Section: Hypergraph Learningmentioning

confidence: 99%

Hypergraph-Enhanced Textual-Visual Matching Network for Cross-Modal Remote Sensing Image Retrieval via Dynamic Hypergraph Learning

Yao

Sun

Liu

et al. 2023

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

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“…Recent concepts that utilize both spatial and spectral information have shown improved classification performance over the spectral centric of BS schemes [ 53 , 54 , 55 ]. There are various different methodologies for implementing these concepts in BS: Clustering based [ 13 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 ], deep learning methods [ 65 , 66 , 67 , 68 , 69 , 70 ], machine learning methods [ 71 , 72 , 73 , 74 ], and a hierarchy of several methodologies combined together [ 75 , 76 ], have been widely reported. Furthermore, some of these require supervision in which training data are needed to optimize the model [ 77 , 78 ], while others are unsupervised without the need of prior information.…”

Section: Introductionmentioning

confidence: 99%

Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications

et al. 2020

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Despite the numerous band selection (BS) algorithms reported in the field, most if not all have exhibited maximal accuracy when more spectral bands are utilized for classification. This apparently disagrees with the theoretical model of the ‘curse of dimensionality’ phenomenon, without apparent explanations. If it were true, then BS would be deemed as an academic piece of research without real benefits to practical applications. This paper presents a spatial spectral mutual information (SSMI) BS scheme that utilizes a spatial feature extraction technique as a preprocessing step, followed by the clustering of the mutual information (MI) of spectral bands for enhancing the efficiency of the BS. Through the SSMI BS scheme, a sharp ’bell’-shaped accuracy-dimensionality characteristic that peaks at about 20 bands has been observed for the very first time. The performance of the proposed SSMI BS scheme has been validated through 6 hyperspectral imaging (HSI) datasets (Indian Pines, Botswana, Barrax, Pavia University, Salinas, and Kennedy Space Center (KSC)), and its classification accuracy is shown to be approximately 10% better than seven state-of-the-art BS schemes (Saliency, HyperBS, SLN, OCF, FDPC, ISSC, and Convolution Neural Network (CNN)). The present result confirms that the high efficiency of the BS scheme is essentially important to observe and validate the Hughes’ phenomenon in the analysis of HSI data. Experiments also show that the classification accuracy can be affected by as much as approximately 10% when a single ‘crucial’ band is included or missed out for classification.

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Local-View-Assisted Discriminative Band Selection With Hypergraph Autolearning for Hyperspectral Image Classification

Cited by 17 publications

References 59 publications

Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection

Multiscale Fusion Network Based on Global Weighting for Hyperspectral Feature Selection

Hypergraph-Enhanced Textual-Visual Matching Network for Cross-Modal Remote Sensing Image Retrieval via Dynamic Hypergraph Learning

Spatial Spectral Band Selection for Enhanced Hyperspectral Remote Sensing Classification Applications

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