Superpixel-guided multifeature tensor for hyperspectral image classification with limited training samples

Wang, Peng; Cheng-yong, Zheng; Liu, Saihua

doi:10.1016/j.optlastec.2022.109020

Cited by 4 publications

(1 citation statement)

References 42 publications

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“…As an essential pre-processing stage for many applications 1 – 4 in computer vision and image processing, superpixel segmentation has attracted considerable attention over the past decade. Its purpose is to group pixels with similar features into clusters, providing a more concise representation of an image and reducing the complexity of subsequent tasks.…”

Section: Introductionmentioning

confidence: 99%

Superpixel segmentation of high-resolution remote sensing image based on feature reconstruction method by salient edges

Wang

et al. 2023

J. Appl. Rem. Sens.

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Superpixel segmentation is an important image preprocessing technology that can aggregate adjacent pixels with similar characteristics to reduce the complexity of subsequent tasks. However, with the increasing spatial resolution of remote sensing images, maintaining accurate boundary information of ground objects during superpixel segmentation has become a challenge. We propose a feature reconstruction method based on edge detection and adaptive morphological reconstruction to enhance feature representation for superpixel segmentation. Specifically, we employ the structured forest model to extract robust edges from the image, and an adaptive morphological reconstruction method is proposed to eliminate noise from the edge information. Furthermore, a feature fusion method based on wavelet transform is constructed to integrate the extracted edge information with the original image, which further enhances the segmentation accuracy of image edges in superpixel segmentation. Experimental results on three datasets of natural and remote sensing images demonstrate that the proposed method can effectively construct the feature representation of images and achieve better superpixel segmentation results than other methods.

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Section: Introductionmentioning

confidence: 99%

Superpixel segmentation of high-resolution remote sensing image based on feature reconstruction method by salient edges

Wang

et al. 2023

J. Appl. Rem. Sens.

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Unsupervised multi-level spatio-spectral fusion transformer for hyperspectral image super-resolution

Cao,

Lian,

et al. 2024

Optics & Laser Technology

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Mayfly algorithm-based semi-supervised band selection with enhanced bitonic filter for spectral-spatial hyperspectral image classification

Moharram,

Sundaram

2024

International Journal of Remote Sensing

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Superpixel-guided multifeature tensor for hyperspectral image classification with limited training samples

Cited by 4 publications

References 42 publications

Superpixel segmentation of high-resolution remote sensing image based on feature reconstruction method by salient edges

Superpixel segmentation of high-resolution remote sensing image based on feature reconstruction method by salient edges

Unsupervised multi-level spatio-spectral fusion transformer for hyperspectral image super-resolution

Mayfly algorithm-based semi-supervised band selection with enhanced bitonic filter for spectral-spatial hyperspectral image classification

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