Multi-sensor multispectral reconstruction framework based on projection and reconstruction

Li, Tianshuai; Liu, Tianzhu; Li, Xian; Gu, Yanfeng; Wang, Yukun; Chen, Yushi

doi:10.1007/s11432-023-3910-y

Cited by 2 publications

(1 citation statement)

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“…The DsTer network [28] combines Transformer and ResNet networks, considering the learning of remote interaction information in images, and finally achieves spectral super-resolution of multispectral remote sensing images. Li et al [29] proposed a multi-sensor SR framework (MSSRF) based on a two-step approach in which the problems of amplitude inconsistency and band information extraction are solved using an ideal projection network and an ideal multi-sensor SR network, respectively. And Li and Gu et al introduced a progressive spatial-spectral joint network (PSJN) composed of a 2-D spatial feature extraction module, a 3-D progressive spatial-spectral feature construction module, and a spectral postprocessing module [30].…”

Section: Spectral Super-resolution Based On Deep Learningmentioning

confidence: 99%

A Multi-Hyperspectral Image Collaborative Mapping Model Based on Adaptive Learning for Fine Classification

Zhang,

Liu,

Zhang

et al. 2024

Remote Sensing

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Hyperspectral (HS) data, encompassing hundreds of spectral channels for the same area, offer a wealth of spectral information and are increasingly utilized across various fields. However, their limitations in spatial resolution and imaging width pose challenges for precise recognition and fine classification in large scenes. Conversely, multispectral (MS) data excel in providing spatial details for vast landscapes but lack spectral precision. In this article, we proposed an adaptive learning-based mapping model, including an image fusion module, spectral super-resolution network, and adaptive learning network. Spectral super-resolution networks learn the mapping between multispectral and hyperspectral images based on the attention mechanism. The image fusion module leverages spatial and spectral consistency in training data, providing pseudo labels for spectral super-resolution training. And the adaptive learning network incorporates spectral response priors via unsupervised learning, adjusting the output of the super-resolution network to preserve spectral information in reconstructed data. Through the experiment, the model eliminates the need for the manual setting of image prior information and complex parameter selection, and can adjust the network structure and parameters dynamically, eventually enhancing the reconstructed image quality, and enabling the fine classification of large-scale scenes with high spatial resolution. Compared with the recent dictionary learning and deep learning spectral super-resolution methods, our approach exhibits superior performance in terms of both image similarity and classification accuracy.

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Section: Spectral Super-resolution Based On Deep Learningmentioning

confidence: 99%