Hyperspectral imaging (HSI) is a powerful tool that can provide substantial information about a scene through remote sensing. This project addresses different challenges in the pipeline of capturing and transmitting, and identifying remote scenes using hyperspectral images. One of the main significant challenges in hyperspectral imaging is analyzing the data and extracting the required information. This is mainly because of the extremely high dimensionality of hyperspectral images, which limits our ability to identify spectral signatures fast and accurately. In addition, noise in hyperspectral images makes matters even more complicated.
- Mohammad Amin Arab
- Kiana Calagari
- Mohamed Hefeeda
Adaptive Identification of Remote Scenes using HSI
Abstract In this project we aim to thoroughly investigate and explore the scene through remote sensing. We do so by using remotely controlled drones to capture videos of a desired site. The captured information will be then transferred to a base station, where it will be processed and analyzed. As the desired site may be a remote outland with limited available bandwidth and given the size of hyperspectral data, all our methods and techniques are designed adaptively. Our methods prioritize data transfer based on the amount of detail required such that maximum accuracy is achieved using a specific amount of transferred data. This is in contrast to the state-of-the-art methods, which assume having all the data is in hand before starting the analysis.