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首页> 外文期刊>Signal processing >Image fusion employing adaptive spectral-spatial gradient sparse regularization in UAV remote sensing
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Image fusion employing adaptive spectral-spatial gradient sparse regularization in UAV remote sensing

机译：无人机遥感中基于光谱空间梯度稀疏正则化的图像融合

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摘要

Unmanned aerial vehicle (UAV) remote sensing has been widely used in vegetation phenotypes and precision agriculture.The fusion of UAV multispectral and panchromatic images has considerable research value.For example.an accurate vegetation index can be obtained.However.large geometrical distortions are observed in UAV images.contributing to the insufficiency of existing fusion algorithms.Spectrum consistency.which indicates that changes in spectral direction are always a smooth function.is investigated in this paper to solve the above problem.Spatial adaptivity is also introduced to reduce spectral distortion in the fusion process.Based on the two aspects.a multispectral and panchromatic image fusion model employing adaptive spectral-spatial gradient sparse regularization is proposed for UAV remote sensing.The separable approximation and augmented Lagrangian methods are employed to optimize this model.In the experiments.the proposed method is firstly compared with other state-of-the-art fusion algorithms.and good performance is verified by UAV datasets in terms of visual effect and objective quality analysis.Secondly.the fusion algorithm is applied in the application of a vegetation phenotype.The experiments finally demonstrate that accurate vegetation indices can be generated by adopting the proposed algorithm.This finding proves the substantial research value of the proposed algorithm in UAV remote sensing.
机译：无人飞行器（UAV）遥感已广泛用于植被表型和精确农业中，UAV多光谱和全色图像的融合具有相当大的研究价值，例如可以获得准确的植被指数，但是观察到较大的几何畸变。为了解决上述问题，本文研究了光谱一致性，这表明光谱方向的变化始终是一个平滑函数，从​​而解决了上述问题。基于两个方面，提出了一种采用自适应光谱空间梯度稀疏正则化的多光谱和全色图像融合模型用于无人机遥感，并采用可分离近似和增强拉格朗日方法对该模型进行了优化。首先将提出的方法与其他最新技术进行比较t融合算法，通过视觉效果和客观质量分析，通过无人机数据集验证了其良好的性能。其次，将融合算法应用于植被表型的应用，实验表明采用该融合算法可以产生准确的植被指数。这一发现证明了该算法在无人机遥感中的重大研究价值。

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