锐化
高光谱成像
计算机科学
人工智能
多光谱图像
模式识别(心理学)
卷积神经网络
一般化
深度学习
降维
传感器融合
维数(图论)
计算机视觉
数学
数学分析
纯数学
作者
Renwei Dian,Anjing Guo,Shutao Li
标识
DOI:10.1109/tpami.2023.3279050
摘要
Fusing hyperspectral images (HSIs) with multispectral images (MSIs) of higher spatial resolution has become an effective way to sharpen HSIs. Recently, deep convolutional neural networks (CNNs) have achieved promising fusion performance. However, these methods often suffer from the lack of training data and limited generalization ability. To address the above problems, we present a zero-shot learning (ZSL) method for HSI sharpening.Specifically, we first propose a novel method to quantitatively estimate the spectral and spatial responses of imaging sensors with high accuracy. In the training procedure, we spatially subsample the MSI and HSI based on the estimated spatial response and use the downsampled HSI and MSI to infer the original HSI. In this way, we can not only exploit the inherent information in the HSI and MSI, but the trained CNN can also be well generalized to the test data. In addition, we take the dimension reduction on the HSI, which reduces the model size and storage usage without sacrificing fusion accuracy. Furthermore, we design an imaging model-based loss function for CNN, which further boosts the fusion performance.The experimental results show the significantly high efficiency and accuracy of our approach. The code can be obtained at https://github.com/renweidian .
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