Improving the Quality and Spatial Resolution of Super-spectral and Hyper-spectral Images through Sensor Fusion

通过传感器融合提高超光谱和高光谱图像的质量和空间分辨率

• 批准号: RGPIN-2016-03662
• 负责人: Zhang, Yun
• 金额: $ 3.06万
• 依托单位: University of New Brunswick
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=688487
• 关键词: Improving; Quality; Spatial; Resolution; Super

As much as 80 percent of the information used for decision making has some geospatial context. Multi-spectral satellites (with up to 10 spectral bands) have been increasingly launched in recent years for quickly capturing information of the earth's surface. It has been proven that super-spectral (10 to 50 spectral bands) and hyper-spectral (50 to 250 bands) remote sensing can provide much more information than multi-spectral remote sensing, because of their higher spectral resolutions (more spectral bands). *** However, due to the inherent limitation of electro-optic sensor physics, it is impossible to increase the number of the spectral bands of a given sensor without losing its image quality or spatial resolution or both. Therefore, very few super-spectral or hyper-spectral satellite missions have been launched to date. *** The proposed research will develop an alternative, sensor fusion solution to overcome the limitation of sensor physics. The solution will use or simulate two or more sensors to capture complementary information respectively (i.e. either high quality, high spatial resolution but low spectral resolution information, or high quality, high spectral resolution but low spatial resolution information), and then fuse the complementary information together to produce a super-spectral or hyper-spectral image that contains high quality, high spatial resolution and high spectral resolution information. *** New algorithms and software systems will be developed in the proposed research. The super-spectral images from WorldView-3 satellite and hyper-spectral images from EO-1 satellite will be used as experiment data. WorldView-3 was launched in August 2014. EO-1 satellite was launched in 2000 as part of NASA's New Millennium Program to enable the development of future earth imaging technologies. They are excellent data source for the proposed research. *** Based on our international success in increasing the spatial resolution of multi-spectral sensors through the fusion of the information from a panchromatic sensor (with 1 broad spectral band) and a multi-spectral sensor (with up to 8 spectral bands), it is anticipated that our new solution will be able to increase the spatial resolution of the super-spectral images of WorldView-3 (with 16 spectral bands), and increase the quality and spatial resolution of the hyper-spectral images of EO-1 (with 220 spectral bands). *** The successful outcome from this research will improve the image quality and spatial resolution of existing sensor systems. More importantly, it will contribute to the advancement of future Canadian and international super-spectral and hyper-spectral space missions. *****

多达80%的用于决策的信息都有一定的地理空间背景。近年来，越来越多的多光谱卫星（具有多达10个光谱带）被发射，用于快速捕获地球表面的信息。事实证明，超光谱（10至50个光谱波段）和超光谱（50至250个波段）遥感可以提供比多光谱遥感多得多的信息，因为它们具有更高的光谱分辨率（更多的光谱波段）。*** 然而，由于电光传感器物理的固有限制，不可能在不损失其图像质量或空间分辨率或两者的情况下增加给定传感器的光谱带的数量。因此，迄今为止发射的超光谱或超光谱卫星飞行任务很少。*** 拟议的研究将开发一种替代的传感器融合解决方案，以克服传感器物理的限制。该解决方案将使用或模拟两个或多个传感器来分别捕获互补信息（即，高质量、高空间分辨率但低光谱分辨率信息，或者高质量、高光谱分辨率但低空间分辨率信息），然后将互补信息融合在一起以产生包含高质量的超光谱或超光谱图像，高空间分辨率和高光谱分辨率信息。*** 新的算法和软件系统将在拟议的研究开发。利用WorldView-3卫星的超光谱图像和EO-1卫星的超光谱图像作为实验数据。WorldView-3于2014年8月推出。EO-1卫星于2000年发射，是NASA新千年计划的一部分，旨在开发未来的地球成像技术。它们是拟议研究的优秀数据源。*** 基于我们在国际上成功地通过融合来自全色传感器的信息来提高多光谱传感器的空间分辨率，（1个宽光谱带）和一个多光谱传感器（多达8个光谱波段），预计我们的新解决方案将能够提高WorldView-3超光谱图像的空间分辨率（16个光谱波段），并提高EO-1高光谱图像（220个光谱波段）的质量和空间分辨率。*** 这项研究的成功结果将提高现有传感器系统的图像质量和空间分辨率。更重要的是，它将有助于推进加拿大和国际未来的超光谱和超光谱空间飞行任务。*****