Soft Contrastive Representation Learning for Cloud-Particle Images Captured In-Flight by the New HVPS-4 Airborne Probe

Abstract

Cloud properties underpin accurate climate modeling and are often derived from the individual particles comprising a cloud. Studying these cloud particles is challenging due to their intricate shapes, called “habits,” and manual classification via probe-generated images is time-consuming and subjective. We propose a novel method for habit representation learning that uses minimal labeled data by leveraging self-supervised learning (SSL) with Vision Transformers (ViTs) on a newly acquired dataset of 124000 images captured by the novel high-volume precipitation spectrometer ver. 4 (HVPS-4) probe. Our approach significantly outperforms ImageNet pretraining by 48% on a 293-sample annotated dataset. Notably, we present the first SSL scheme for learning habit representations, leveraging data collected in flight from the probe. Our results demonstrate that self-supervised pretraining significantly improves habit classification even when using single-channel HVPS-4 data. We achieve further gains using sequential views and a soft contrastive objective tailored for sequential, in-flight measurements. Our work paves the way for applying SSL to multiview and multiscale data from advanced cloud-particle imaging probes, enabling comprehensive characterization of the flight environment. We publicly release data, code, and models associated with this study.