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高光谱图像的遥感性能增强。

Remote Sensing Performance Enhancement in Hyperspectral Images.

机构信息

Signal Processing, Inc., Rockville, MD 20850, USA.

出版信息

Sensors (Basel). 2018 Oct 23;18(11):3598. doi: 10.3390/s18113598.

DOI:10.3390/s18113598
PMID:30360507
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6263628/
Abstract

Hyperspectral images with hundreds of spectral bands have been proven to yield high performance in material classification. However, despite intensive advancement in hardware, the spatial resolution is still somewhat low, as compared to that of color and multispectral (MS) imagers. In this paper, we aim at presenting some ideas that may further enhance the performance of some remote sensing applications such as border monitoring and Mars exploration using hyperspectral images. One popular approach to enhancing the spatial resolution of hyperspectral images is pansharpening. We present a brief review of recent image resolution enhancement algorithms, including single super-resolution and multi-image fusion algorithms, for hyperspectral images. Advantages and limitations of the enhancement algorithms are highlighted. Some limitations in the pansharpening process include the availability of high resolution (HR) panchromatic (pan) and/or MS images, the registration of images from multiple sources, the availability of point spread function (PSF), and reliable and consistent image quality assessment. We suggest some proactive ideas to alleviate the above issues in practice. In the event where hyperspectral images are not available, we suggest the use of band synthesis techniques to generate HR hyperspectral images from low resolution (LR) MS images. Several recent interesting applications in border monitoring and Mars exploration using hyperspectral images are presented. Finally, some future directions in this research area are highlighted.

摘要

高光谱图像具有数百个光谱波段，已被证明在物质分类方面具有出色的性能。然而，尽管硬件技术得到了大力发展，但其空间分辨率仍相对较低，与彩色和多光谱（MS）成像仪相比。在本文中，我们旨在提出一些想法，以进一步提高使用高光谱图像进行边境监测和火星探测等遥感应用的性能。提高高光谱图像空间分辨率的一种流行方法是 pansharpening。我们简要回顾了最近的图像分辨率增强算法，包括单超分辨率和多图像融合算法，用于高光谱图像。强调了增强算法的优点和局限性。pan sharpening 过程中的一些限制包括高分辨率（HR）全色（pan）和/或 MS 图像的可用性、来自多个源的图像的配准、点扩散函数（PSF）的可用性以及可靠和一致的图像质量评估。我们建议了一些积极的想法，以在实践中缓解上述问题。在没有高光谱图像的情况下，我们建议使用波段合成技术，从低分辨率（LR）MS 图像生成 HR 高光谱图像。介绍了一些使用高光谱图像在边境监测和火星探测方面的最新有趣应用。最后，强调了该研究领域的一些未来方向。

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