Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature

Spectral Super-Resolution Reconstruction of Multispectral Remote Sensing Images via Clustering-Based Spectral Feature

Hyperspectral (HS) remote sensing imagery has broad applications but faces challenges in acquiring high-quality wide-swath data due to sensor hardware limitations. To address this, we proposed a jointly fused convolutional neural network for spectral super-resolution (JF-CNNSSR), leveraging spectral...

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Bibliographic Details
Main Authors: Wang Benlin, Yu Qinglin, Wang Zuo, Li Weitao, Wang Yong, Liu Huan, Gu Shuangxi, Zhang Lingling, Lv Dong
Format: Article
Language:English
Published: IEEE 2025-01-01
Series:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Subjects:
Deep learning
hyperspectral imaging (HSI)
image fusion
multispectral imaging (MSI)
spectral super-resolution (SSR)
Online Access:https://ieeexplore.ieee.org/document/11045064/
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Summary:Hyperspectral (HS) remote sensing imagery has broad applications but faces challenges in acquiring high-quality wide-swath data due to sensor hardware limitations. To address this, we proposed a jointly fused convolutional neural network for spectral super-resolution (JF-CNNSSR), leveraging spectral reflectance variations across different land cover types. The method employs paired overlapping multispectral (MS) and HS images to develop two spectral mapping networks: a 1-D-CNN spectral mapping network and a 3-D-CNN spectral mapping network, trained class-specifically on clustered spectral samples. A spectral distance-based joint weighting mechanism integrates outputs from both networks to synthesize HS imagery spatially consistent with the input MS data. Experiments on four benchmark datasets demonstrate that JF-CNNSSR outperforms existing spectral super-resolution methods, achieving significant improvements across key quantitative metrics including peak signal-to-noise ratio, spectral angle mapper, structural similarity index measure, ERGAS, and MSE. Practical validation through grassland coverage estimation in China’s Three-River Source Region further confirms the superiority of reconstructed HS imagery over original MS data in ecological application accuracy.
ISSN:1939-1404
2151-1535

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