Advanced machine learning models and methods for hyperspectral imagery processing and analysis

高光谱成像利用数百个狭窄且连续的光谱图像波段记录来自地球表面的反射辐射，可以为识别和区分光谱相似材料提供丰富的信息，并已成为政府机构、研究机构和商业公司在各种资源和环境应用中识别材料和绘制表面特性的重要工具。然而，由于高光谱图像（HSI）数据量大、固有的高维性、空间光谱异质性和噪声效应，操作用户和研究科学家如何高效、准确地将HSI转化为增值信息面临着巨大的挑战。因此，用于大规模复杂 HSI 的计算机辅助处理和分析的新模型和方法可以显着提高有价值的地球化学、生物化学和生物物理变量的量化，从而改善自然资源管理和环境监测。 拟议的研究计划旨在开发用于高光谱图像处理和分析的新型智能模型和计算方法。将研究以下目标：1）一种新型的基于光谱分解的 HSI 表示框架，具有强大的建模能力和高效的优化方法，能够充分解决 HSI 的各种关键特征，并解开 HSI 中的潜在解释因素，以支持增强型任务特定算法的开发，2）针对不同 HSI 处理任务（例如去噪、超分辨率、特征提取、分类、分解、末端成员提取、子像素映射）量身定制的高精度和高效算法，以实现全面的 HSI 处理任务。探索HSI的潜力，3）先进的HSI软件工具利用优化的图像处理算法，促进开发关键HSI应用程序的数据处理工作流程、管道和分析解决方案，以挖掘HSI中的增值信息。拟议的研究计划将改进当前 HSI 建模理论、算法和软件工具的最新水平，从而提高环境监测、评估和保护以及自然资源管理和勘探方面的新能力。此外，HQP 将在多学科环境中接受高光谱图像处理和分析、遥感、计算机视觉和人工智能方面的培训，使他们在价值数十亿美元的就业市场中处于高需求，并使他们能够在加拿大工业界、政府和学术界找到相关的领导机会。

Hyperspectral imaging, which records the reflected radiation from the earth surface using hundreds of narrow and contiguous spectral image bands, can provide rich information for identifying and distinguishing spectrally similar materials, and has become an essential tool leveraged by government agencies, research institutions and commercial companies to discern materials and map surface properties in various resource and environment applications. Nevertheless, due to the large data volume, the innate high-dimensionality, spatial-spectral heterogeneities and the noise effect of hyperspectral image (HSI), there are significant challenges for operational users and research scientists to efficiently and accurately transform HSI into value-added information. Therefore, novel models and methods for computer-aided processing and analysis of the large-volume complex HSI can significantly improve the quantification of valuable geochemical, biochemical and biophysical variables, and thereby improve natural resource management and environment monitoring. The proposed research program aims to develop novel intelligent models and computational methods for hyperspectral image processing and analysis. The following objectives will be investigated: 1) a novel spectral unmixing based HSI representation framework with strong modeling capacity and efficient optimization approach, capable of fully addressing various key characteristics of HSI, and disentangling the underlying explanatory factors in HSI for supporting the development of enhanced task-specific algorithms, 2) highly accurate and efficient algorithms tailored to different HSI processing tasks (e.g., denoising, super-resolution, feature extraction, classification, unmixing, end-member extraction, sub-pixel mapping) to enable full exploration of the potential of HSI, 3) advanced HSI software tools leveraging the optimized image processing algorithms to facilitate the development of data processing workflows, pipelines and analytic solutions to key HSI applications for mining value-added information in HSI. The proposed research program will improve the current state-of-the-arts in HSI modeling theories, algorithms and software tools, and thus advance new capabilities in environment monitoring, assessment and protection, as well as natural resource management and exploration. Moreover, HQP will be trained in hyperspectral image processing and analysis, remote sensing, computer vision and artificial intelligence within a multidisciplinary environment, making them in high demand in the billion dollar job market, and enabling them to find relevant, leadership opportunities in Canadian industry, government and academia.