权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

4D-HSI-4-Free: Integrating Sensors & Vision Process Engineering to Deliver a Tool for revealing 3D Hyperspectral Agri-data from 2D Images

4D-HSI-4-Free：集成传感器

基本信息

批准号：
BB/N02107X/1
负责人：
Bruce Grieve
金额：
$ 19.07万
依托单位：
University of Manchester
依托单位国家：
英国
项目类别：
Research Grant
财政年份：
2016
资助国家：
英国
起止时间：
2016 至无数据
项目状态：
已结题

来源：
https://gtr.ukri.org/projects?ref=BB%2FN02107X%2F1
关键词：
4D HSI Free Integrating Sensors

项目摘要

Hyperspectral and multispectral imaging for crop phenotyping and stress analysis has seen significant growth in recent years for wide area scanning, notably from satellites and aircraft. This has been principally based on variants of the Normalised Difference Vegetation Index, 'red edge', ratios and / or chlorophyll fluorescence bands. Though more subtle agri-analysis is being introduced within these remote monitoring technologies, notably with the launch of the ESA Sentinel family of satellites, the reliance on sunlit imaging and the physical engineering limits on resolving power of the optics, alongside atmospheric variations in absorbance and refraction, set hard boundaries on the spatial and spectral sensitivity.In more recent years Close-Proximity Hyperspectral Imaging (CP-HSI) has been progressed by a number of research groups, in order to ground-truth remotely collected images as well as to gain data with greater subtlety and differentiating power than can be obtained from the remote platforms, due to the physical constraints described. Additionally, CP-HSI offers the potential for greater data availability by avoiding the issues of cloud-cover, and other obstructions, as well as the lack of access to images whilst satellites are orbiting. These CP-HSI studies have tended to adopt the same high-cost passive HSI instruments, as used by the geospatial imaging sector, and then take advantage of the variations in plant and soil data arising intra- and inter-crop canopy to identify the new agri-features.The e-Agri research team at the University of Manchester have worked over recent years on translating the above concepts into lower-cost and miniaturised engineered systems through replacing the passive HSI instrumentation with active systems, based on broadband proprietary silicon imaging detectors coupled with narrowband LED sources. The resulting Active Close-Proximity (ACP) HSI systems are not only several orders of magnitude cheaper and higher spatial resolution, than their remote passive equivalents, they also offer spectral signal-to-noise that can far exceed what is possible from the passive instruments.Encouraging as this is, there are engineering challenges associated with active CP-HSI, not least the need to compensate for the orientation affects from position of the biological specimen with respect to the sensor system as well prevent corruption of the spectral data by variations in the lighting location and optical power of the LEDs. These are reconcilable through integrating the optical engineering of multiple sources alongside Photometric Stereo (PS) image reconstruction. The PS technique has arisen as an alternate technique to structured-light for delivering surface normals and texture information at unprecedented spatial resolution, while using inherently low-cost equipment. PS involves capturing a number of images (3+) of an object under varying but known lighting, with each image providing a constraint on the orientation at each pixel. Work pioneered by the CMV has allowed the PS technique to move beyond a laboratory setting towards real-word applications, by relaxing prior assumptions, such as Lambertian surface reflection, collimated illumination and by extending the technique to moving applications.The team will deliver a new class of 4-Dimensional imaging within an accessible tool which will only marginally increases the component cost versus the 2D hyperspectral precursor. The project is achievable as it will be accelerated via the e-Agri UoM team partnering with the Centre for Machine Vision, at the Bristol Robotics Laboratory, to translate their PS algorithms so that they may be integrated within the ACP-HSI sensors research. The merger of these capabilities will prove the viability of a miniaturised 4D imaging system and deliver a proof-of-principal prototype system for characterisation against plant, animal and aquatic samples.

近年来，用于作物表型分析和应力分析的高光谱和多光谱成像在大面积扫描方面有了显著增长，特别是来自卫星和飞机的扫描。这主要是基于归一化差异植被指数、“红边”、比率和/或叶绿素荧光带的变体。虽然在这些远程监测技术中正在引入更微妙的农业分析，特别是随着欧空局哨兵系列卫星的发射，但对阳光成像的依赖和光学分辨率的物理工程限制，以及大气吸收和折射的变化，在空间和光谱灵敏度上设置硬边界。近年来，许多研究小组已经对近距离超光谱成像（CP-HSI）进行了改进，以便对远程收集的图像进行实地验证，以及由于所述的物理限制，获得比从远程平台获得的数据更精细和更有区分能力的数据。此外，CP-HSI通过避免云层覆盖和其他障碍物的问题，以及在卫星轨道运行时无法访问图像，提供了更大的数据可用性的潜力。这些CP-HSI研究倾向于采用与地理空间成像部门相同的高成本无源HSI仪器，然后利用作物冠层内和作物冠层间产生的植物和土壤数据的变化来识别新的农业特征。曼彻斯特大学的e-Agri研究小组近年来一直致力于将上述概念转化为更低的农业特征。通过将被动HSI仪器替换为主动系统，基于宽带专有硅成像探测器与窄带LED光源相结合，实现了高成本和低成本的工程系统。由此产生的主动近距离（ACP）HSI系统不仅比其远程被动等效物便宜几个数量级且空间分辨率更高，而且还提供远远超过被动仪器的光谱信噪比。令人鼓舞的是，存在与主动CP-HSI相关的工程挑战，尤其是需要补偿来自生物样本相对于传感器系统的位置的取向影响，以及防止光谱数据由于LED的照明位置和光功率的变化而被破坏。通过将多个光源的光学工程与光度立体（PS）图像重建相结合，这些都是可以调和的。PS技术已经作为结构光的替代技术出现，用于以前所未有的空间分辨率提供表面法线和纹理信息，同时使用固有的低成本设备。PS涉及在变化但已知的照明下捕获对象的多个图像（3+），其中每个图像提供对每个像素处的取向的约束。CMV开创的工作使PS技术超越了实验室设置，走向实际应用，通过放松先前的假设，如朗伯表面反射，准直照明，并将该技术扩展到移动应用。该团队将在一个可访问的工具中提供一种新的4维成像，与2D高光谱先驱相比，这只会略微增加组件成本。该项目是可以实现的，因为它将通过与机器视觉中心合作的e-Agri UoM团队加速，在布里斯托机器人实验室，翻译他们的PS算法，以便他们可以集成在ACP-HSI传感器研究中。这些功能的合并将证明一个可扩展的4D成像系统的可行性，并提供一个主要的原型系统，用于对植物，动物和水生样品进行表征。

项目成果

期刊论文数量（8）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

BRDF of human skin in the visible spectrum

可见光谱中人体皮肤的 BRDF

DOI：
10.1108/sr-11-2016-0258
发表时间：
2017
期刊：
Sensor Review
影响因子：
1.6
作者：
Sohaib A
通讯作者：
Sohaib A

Feature-Ensemble-Based Novelty Detection for Analyzing Plant Hyperspectral Datasets

DOI：
10.1109/jstars.2017.2788426
发表时间：
2018-04-01
期刊：
IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
影响因子：
5.5
作者：
AlSuwaidi, Ali;Grieve, Bruce;Yin, Hujun
通讯作者：
Yin, Hujun

Photometric stereo for three-dimensional leaf venation extraction.