CromSS: Cross-Modal Pre-Training with Noisy Labels for Remote Sensing Image Segmentation

Abstract

We study the potential of noisy labels y to pretrain semantic segmentation models in a multi-modal learning framework for geospatial applications. Specifically, we propose a novel Cross-modal Sample Selection method (CromSS) that utilizes the class distributions $P^{(d)}(x,c)$ over pixels $x$ and classes c modelled by multiple sensors/modalities $d$ of a given geospatial scene. Consistency of predictions across sensors d is jointly informed by the entropy of $P^{(d)}(x,c)$. Noisy label sampling we determine by the confidence of each sensor d in the noisy class label, $P^{(d)}(x,c=y(x))$. To verify the performance of our approach, we conduct experiments with Sentinel-1 (radar) and Sentinel-2 (optical) satellite imagery from the globally-sampled SSL4EO-S12 dataset. We pair those scenes with 9-class noisy labels sourced from the Google Dynamic World project for pretraining. Transfer learning evaluations (downstream task) on the DFC2020 dataset confirm the effectiveness of the proposed method for remote sensing image segmentation.