Hyperspectral Image Fusion Algorithm Based on Improved Deep Residual Network

Li, Yuxuan; Fu, Minyue; Zhang, Hanfeng; Xu, Han; Zhang, Qi

doi:10.1016/j.sigpro.2023.109058

Cited by 4 publications

(1 citation statement)

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“…These methods include pixel-level fusion, feature-level fusion, and decision-level fusion. The spatial and spectral properties were enhanced by merging them at four scales using a lightweight deep CNN model based on residuals, as demonstrated by Li et al [ 21 ]. In a similar study, Wang et al [ 22 ] improved the spatial information of HSI by multispectral image through cross-modality information extracted by a multi-hierarchical cross transformer (MCT).…”

Section: Introductionmentioning

confidence: 99%

Advancing Hyperspectral Image Analysis with CTNet: An Approach with the Fusion of Spatial and Spectral Features

Yadav,

Kumar,

Jalal

et al. 2024

Sensors

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Hyperspectral image classification remains challenging despite its potential due to the high dimensionality of the data and its limited spatial resolution. To address the limited data samples and less spatial resolution issues, this research paper presents a two-scale module-based CTNet (convolutional transformer network) for the enhancement of spatial and spectral features. In the first module, a virtual RGB image is created from the HSI dataset to improve the spatial features using a pre-trained ResNeXt model trained on natural images, whereas in the second module, PCA (principal component analysis) is applied to reduce the dimensions of the HSI data. After that, spectral features are improved using an EAVT (enhanced attention-based vision transformer). The EAVT contained a multiscale enhanced attention mechanism to capture the long-range correlation of the spectral features. Furthermore, a joint module with the fusion of spatial and spectral features is designed to generate an enhanced feature vector. Through comprehensive experiments, we demonstrate the performance and superiority of the proposed approach over state-of-the-art methods. We obtained AA (average accuracy) values of 97.87%, 97.46%, 98.25%, and 84.46% on the PU, PUC, SV, and Houston13 datasets, respectively.

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Section: Introductionmentioning

confidence: 99%