Concurrent Band Selection and Traversability Estimation From Long-Wave Hyperspectral Imagery in Off-Road Settings

Florence Yellin, Scott McCloskey, Cole Hill, Eric Smith, Brian Clipp; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 7483-7492

Autonomous navigation has become increasingly popular in recent years; However, most existing methods focus on on-road navigation and utilize active sensors, such as LiDAR. This paper instead focuses on autonomous off-road navigation using traversability estimation from passive sensors, specifically long-wave (LW) hyperspectral imagery (HSI). We present a method for selecting a subset of hyperspectral bands that are most useful for traversability estimation by designing a band selection module that designs a minimal sensor that measures sparsely-sampled spectral bands while jointly training a semantic segmentation network for traversability estimation. The effectiveness of our method is demonstrated using our dataset of LW HSI from diverse off-road scenes including forest, desert, snow, ponds, and open fields. Our dataset includes imagery collected both during the daytime and nighttime during various weather conditions, including challenging scenes with a wide range of obstacles. Using our method, we learn a small subset (2%) of all the HSI bands that can achieve competitive or better traversability estimation accuracy to that achieved when utilizing all hyperspectral bands. Using only 5 bands, our method is able to achieve a mean class accuracy that is only 1.3% less than that achieved using full 256-band HSI and only 0.1% less than that achieved using 250-band HSI, demonstrating the success of our method.