Plant detection from ultra high resolution remote sensing images: A Semantic Segmentation approach based on fuzzy loss

TL;DR

This paper presents a semantic segmentation approach using fuzzy loss for plant detection in ultra high resolution remote sensing images, introducing a new dataset and improving boundary distinction in plant species identification.

Contribution

The study introduces a fuzzy loss function for semantic segmentation of plant species in UHR remote sensing images, along with a new high-resolution dataset.

Findings

Fuzzy loss improves boundary detection in segmentation masks.

Method shows promising results on both new and public datasets.

Highlights the need for further refinement in plant species segmentation.

Abstract

In this study, we tackle the challenge of identifying plant species from ultra high resolution (UHR) remote sensing images. Our approach involves introducing an RGB remote sensing dataset, characterized by millimeter-level spatial resolution, meticulously curated through several field expeditions across a mountainous region in France covering various landscapes. The task of plant species identification is framed as a semantic segmentation problem for its practical and efficient implementation across vast geographical areas. However, when dealing with segmentation masks, we confront instances where distinguishing boundaries between plant species and their background is challenging. We tackle this issue by introducing a fuzzy loss within the segmentation model. Instead of utilizing one-hot encoded ground truth (GT), our model incorporates Gaussian filter refined GT, introducing stochasticity during training. First experimental results obtained on both our UHR dataset and a public dataset are presented, showing the relevance of the proposed methodology, as well as the need for future improvement.