Hybrid spatial-spectral feature in broad learning system for Hyperspectral image classification

Ma, You; Li, Zhi; Chen, C. L. Philip Chen

doi:10.1007/s10489-021-02320-7

Cited by 8 publications

(2 citation statements)

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“…However, due to the lack of the utilisation of spatial information in BLS, it is usually difficult to obtain satisfactory classification results for HSIs with insufficient marked samples (Chen & Liu, 2018). Therefore, improving BLS performance by augmenting spatial information has been extensively investigated (Ma, Liu, et al, 2022;Ye et al, 2021;Yi et al, 2018;. Along this way, Yi et al first employed hierarchical guidance filtering to obtain a spectral-spatial representation of a HSI, and then applied BLS to classify the HSI with limited labelled samples (Yi et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…However, due to the lack of the utilisation of spatial information in BLS, it is usually difficult to obtain satisfactory classification results for HSIs with insufficient marked samples (Chen & Liu, 2018). Therefore, improving BLS performance by augmenting spatial information has been extensively investigated (Ma, Liu, et al., 2022; Ye et al., 2021; Yi et al., 2018; Zhao et al., 2021). Along this way, Yi et al.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral image classification based on superpixel merging and broad learning system

Xie,

Wang,

Jin

et al. 2024

The Photogrammetric Record

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Most spectral–spatial classification methods for hyperspectral images (HSIs) can achieve satisfactory classification results. However, the common problem faced with these approaches is the need for a long training time and sufficient training samples. To address this issue, this study proposes an effective spectral–spatial HSI classification method based on superpixel merging, superpixel smoothing and broad learning system (SMS‐BLS). The newly introduced parameter‐free superpixel merging technique based on local modularity not only enhances the role of local spatial information in classification, but also maintains class boundary information as much as possible. In addition, the spectral and spatial information of HSIs is further fused during the superpixel smoothing process. As a result, with limited training samples, using merged and smoothed superpixels instead of pixels as input to the broad learning system significantly improves its classification performance. Moreover, the merged superpixels weaken the dependence of the classification results on the superpixel segmentation scale. The effectiveness of the proposed method was validated on three HSI benchmarks, namely Indian Pines, Pavia University and Salinas. Experimental and comparative results show the superiority of the method to other state‐of‐the‐art approaches in terms of overall accuracy and running time.

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Section: Introductionmentioning

confidence: 99%

“…However, due to the lack of the utilisation of spatial information in BLS, it is usually difficult to obtain satisfactory classification results for HSIs with insufficient marked samples (Chen & Liu, 2018). Therefore, improving BLS performance by augmenting spatial information has been extensively investigated (Ma, Liu, et al., 2022; Ye et al., 2021; Yi et al., 2018; Zhao et al., 2021). Along this way, Yi et al.…”

Section: Introductionmentioning

confidence: 99%