ELM-based hyperspectral imagery processor for onboard real-time classification

Hyperspectral imagery is being widely used for accurate object detection and terrain feature classification. Modern imaging spectrometers produce huge amounts of data that are compressed onboard and downloaded to ground stations to be processed. Increasing spectral resolution and data acquisition rates demand more efficient compression techniques to meet downlink bandwidth restrictions. A different approach to reducing data-transfer bottlenecks consists of processing hyperspectral imagery information onboard. Real-time onboard processing would, at the same time, broaden the scope of missions that spacecrafts and aircrafts carrying hyperspectral cameras could fulfill by providing them with immediate decision-making capacity in critical circumstances. This paper investigates the use of Extreme Learning Machines (ELMs) for the classification of high dimensional data, and how specialized hardware and application-specific processor design can help to produce high performance, lightweight, and reduced power consumption systems for onboard hyperspectral imagery processing.