Learning Semantic Segmentation with Query Points Supervision on Aerial Images

TL;DR

This paper introduces a weakly supervised semantic segmentation method for aerial images that uses query point annotations and superpixels, significantly reducing annotation effort while maintaining competitive accuracy.

Contribution

The authors propose a novel weakly supervised learning approach that extends query point labels into superpixels for aerial image segmentation, reducing annotation costs.

Findings

Achieves competitive segmentation performance with limited supervision.

Reduces annotation time and cost compared to fully supervised methods.

Validates approach on aerial datasets across different architectures.

Abstract

Semantic segmentation is crucial in remote sensing, where high-resolution satellite images are segmented into meaningful regions. Recent advancements in deep learning have significantly improved satellite image segmentation. However, most of these methods are typically trained in fully supervised settings that require high-quality pixel-level annotations, which are expensive and time-consuming to obtain. In this work, we present a weakly supervised learning algorithm to train semantic segmentation algorithms that only rely on query point annotations instead of full mask labels. Our proposed approach performs accurate semantic segmentation and improves efficiency by significantly reducing the cost and time required for manual annotation. Specifically, we generate superpixels and extend the query point labels into those superpixels that group similar meaningful semantics. Then, we train semantic segmentation models supervised with images partially labeled with the superpixel pseudo-labels. We benchmark our weakly supervised training approach on an aerial image dataset and different semantic segmentation architectures, showing that we can reach competitive performance compared to fully supervised training while reducing the annotation effort. The code of our proposed approach is publicly available at: https://github.com/santiago2205/LSSQPS.