The analysis of images has been used by the scientific community to solve challenging problems and to get insight into diverse natural, social, and technical phenomena. Different types of images have been employed in various areas of study. One example is the hyperspectral images, which have higher resolution when compared to conventional camera images. Analyzing such images has its challenges. For instance, it is computationally demanding, and traditional methods have some limitations. This project provides an efficient solution to analyze such images, by exploiting high-performance computing tools and machine learning techniques. The resulting methods are applied to image-based atmospheric cloud detection.
The project develops a real time, multi-layer, and modular segmentation framework for hyperspectral images. The developed framework automatically identifies various regions within a hyperspectral image by classifying each pixel of the image and associating them to class segments. The developed system is multi-layer, where each layer’s responsibility is to perform an operation on its input, generate region classification data, and pass the resultant output to the next layer. Importantly, each layer analyzes its input from distinct viewpoints, utilizing spectral and spatial data, resulting in a multi-layer framework where the layers complement each other. Also, this project aims to provide an optimized high-performance (speed-up and accuracy) computational tool for real-time hyperspectral image analysis. This is achieved by adapting the algorithms used in the different parts of the model for parallel processing.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
