Handling Image and Label Resolution Mismatch in Remote Sensing

TL;DR

This paper addresses the challenge of resolution mismatch in remote sensing semantic segmentation by proposing a novel method that leverages low-resolution labels and high-resolution exemplars, improving prediction granularity without high-res annotations.

Contribution

The paper introduces a new approach that uses low-resolution supervision combined with high-resolution exemplars, incorporating region aggregation, adversarial learning, and self-supervised pretraining.

Findings

Effective in handling resolution mismatch in remote sensing images

Produces fine-grained segmentation without high-resolution labels

Demonstrates real-world applicability through extensive experiments

Abstract

Though semantic segmentation has been heavily explored in vision literature, unique challenges remain in the remote sensing domain. One such challenge is how to handle resolution mismatch between overhead imagery and ground-truth label sources, due to differences in ground sample distance. To illustrate this problem, we introduce a new dataset and use it to showcase weaknesses inherent in existing strategies that naively upsample the target label to match the image resolution. Instead, we present a method that is supervised using low-resolution labels (without upsampling), but takes advantage of an exemplar set of high-resolution labels to guide the learning process. Our method incorporates region aggregation, adversarial learning, and self-supervised pretraining to generate fine-grained predictions, without requiring high-resolution annotations. Extensive experiments demonstrate the real-world applicability of our approach.