Research on land use classification of hyperspectral images based on multiscale superpixels

Wang, Hua; Li, Weiwei; Huang, Wei; Niu, Ji Qiang; Nie, Ke

doi:10.3934/mbe.2020275

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…An HSI contains abundant spatial texture features and spectral information features, providing valuable information for various remote sensing applications [1]. Hyperspectral remote sensing is extensively employed in various domains, including but not limited to land use classification [2], land cover mapping [3,4], target detection [5], precision agriculture [6], the identification of building surface materials [7], and geological resource exploration [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

The Classification of Hyperspectral Images: A Double-Branch Multi-Scale Residual Network

Fu,

Chen,

Pirasteh

et al. 2023

Remote Sensing

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With the continuous advancement of deep learning technology, researchers have made further progress in the hyperspectral image (HSI) classification domain. We propose a double-branch multi-scale residual network (DBMSRN) framework for HSI classification to improve classification accuracy and reduce the number of required training samples. The DBMSRN consists of two branches designed to extract spectral and spatial features from the HSI. Thus, to obtain more comprehensive feature information, we extracted additional local and global features at different scales by expanding the network width. Moreover, we also increased the network depth to capture deeper feature information. Based on this concept, we devise spectral multi-scale residuals and spatial multi-scale residuals within a double-branch architecture. Additionally, skip connections are employed to augment the context information of the network. We demonstrate that the proposed framework effectively enhances classification accuracy in scenarios with limited training samples through experimental analysis. The proposed framework achieves an overall accuracy of 98.67%, 98.09%, and 96.76% on the Pavia University (PU), Kennedy Space Center (KSC), and Indian Pines (IP) datasets, respectively, surpassing the classification accuracy of existing advanced frameworks under identical conditions.

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