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Hyperspectral image quality based on convolutional network of multi-scale depth

机译：基于多尺度深度卷积网络的高光谱图像质量

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摘要

Hyperspectral imagery has been widely used in military and civilian research fields such as crop yield estimation, mineral exploration, and military target detection. However, for the limited imaging equipment and the complex imaging environment of hyperspectral images, the spatial resolution of hyperspectral images is still relatively low, which limits the application of hyperspectral images. So, studying the data characteristics of hyperspectral images deeply and improving the spatial resolution of hyperspectral images is an important prerequisite for accurate interpretation and wide application of hyperspectral images. The purpose of this paper is to deal with super-resolution of the hyperspectral image quickly and accurately, and maintain the spectral characteristics of the hyperspectral image, makes the spectral separability of the substrate in the original image remains unchanged after super-resolution processing. This paper first learns the mapping relationship between the spectral difference of low-resolution hyper spectral image and the spectral difference of the corresponding high-resolution hyperspectral image based on multiple scale convolutional neural network, Thus, apply this mapping relationship to the input low-resolution hyperspectral image generally, getting the corresponding high resolution spectral difference. Constrained space by using the image of reconstructed spectral difference, this requires the low resolution hyperspectral image generated by the reconstructed image is to be close to the input low resolution hyperspectral image in space, so that the whole process becomes a closed circulation system where the low-resolution hyperspectral image generation of high-resolution hyperspectral images, then back to low-resolution hyperspectral images. This innovative design further enhances the super resolution performance of the algorithm. The experimental results show that the hyperspectral image super-resolution method based on convolutional neural network improves the input image spatial information, and the super-resolution performance of the model is above 90%, which can maintain the spectral information well. (c) 2019 Elsevier Inc. All rights reserved.
机译：高光谱图像已广泛应用于军事和民用研究领域，如作物产量估算，矿物勘探和军事目标检测。然而，对于有限的成像设备和高光谱图像的复杂成像环境，高光谱图像的空间分辨率仍然相对较低，这限制了高光谱图像的应用。因此，研究深度和改善高光谱图像的空间分辨率的高光谱图像的数据特征是精确解释和广泛应用的重要前提先决条件。本文的目的是快速准确地处理高光谱图像的超级分辨率，并保持超光图像的光谱特性，使得在超分辨率处理之后，原始图像中的基板的光谱分置保持不变。本文首先了解了基于多刻度卷积神经网络的低分辨率超光谱图像的光谱差和相应的高分辨率高光谱图像的频谱差的映射关系，从而应用于输入低分辨率的映射关系高光谱图像通常，获得相应的高分辨率光谱差。通过使用重建光谱差的图像的约束空间，这需要由重建图像产生的低分辨率高光谱图像是在空间中接近输入的低分辨率高光谱图像，使得整个过程成为低电平的闭合循环系统 - 求解高光谱图像产生高分辨率高光谱图像，然后返回低分辨率高光谱图像。这种创新设计进一步增强了算法的超分辨率性能。实验结果表明，基于卷积神经网络的高光谱图像超分辨率方法改善了输入图像空间信息，并且模型的超分辨率性能高于90％，可以阱维持光谱信息。 （c）2019 Elsevier Inc.保留所有权利。

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