On-orbit rule-based and deep learning image segmentation strategies

Abstract

We discuss image segmentation algorithms and additional space considerations for BeaverCube-2, a project under development between the MIT Space Telecommunications, Astronomy, Radiation (STAR) Lab and the Northrop Grumman Corporation that aims to demonstrate the use of an Artificial Intelligence (AI) Computational Accelerator System-on-a-Chip (SoC) on a 3U CubeSat in Low-Earth Orbit (LEO). The processing power afforded by the SoC will allow the use of modern artificial intelligence techniques as part of an Earth observation mission to obtain and process visible and infrared imagery of coastal features.We focus on three algorithms used for cloud segmentation in satellite imagery. These are a luminosity-thresholding method, a random forest method, and an autoencoder-based deep learning method. Our luminosity thresholding method classifies each pixel based on its luminosity and achieved 84% accuracy using 2 MB of memory. Our random forest method contextualizes pixels within a 3 × 3 kernel and classifies them based on the luminosity of each pixel in the kernel -it achieved 90% accuracy, with a memory usage of 700 MB. Finally, our U-Net-based deep learning method achieved 92% accuracy with 1500 MB memory usage, demonstrating modest gains over the two simpler methods, with higher accuracy in snow scenes.

Bench satellite development and testing

“…This access is particularly useful for debugging and measurement purposes. Examples of FlatSats that serve their function during development and operation include: EIRFLAT-1 [4], the EMM FlatSat [5,6] and the OPS-SAT FlatSat [7][8][9].…”

Fast Ocean Front Detection Using Deep Learning Edge Detection Models

“…1. This work is targeted towards MIT's BeaverCube-2 mission [9,10], but the concepts and models can apply to any Earth-observing satellite with onboard compute capability.…”

“…Concept of Operations for a generic Earth observing CubeSat with edge compute capability. The system is able to extract features from images on-orbit[9,10].…”