Augmentation Invariance and Adaptive Sampling in Semantic Segmentation of Agricultural Aerial Images

TL;DR

This paper introduces augmentation invariance and adaptive sampling techniques to improve semantic segmentation of agricultural aerial images, addressing perspective variability and class imbalance.

Contribution

It proposes novel augmentation invariance and adaptive sampling methods tailored for aerial imagery, enhancing segmentation accuracy over existing approaches.

Findings

Improved segmentation performance on Agriculture-Vision dataset.

Enhanced model robustness to perspective and scale variations.

Outperforms current state-of-the-art methods.

Abstract

In this paper, we investigate the problem of Semantic Segmentation for agricultural aerial imagery. We observe that the existing methods used for this task are designed without considering two characteristics of the aerial data: (i) the top-down perspective implies that the model cannot rely on a fixed semantic structure of the scene, because the same scene may be experienced with different rotations of the sensor; (ii) there can be a strong imbalance in the distribution of semantic classes because the relevant objects of the scene may appear at extremely different scales (e.g., a field of crops and a small vehicle). We propose a solution to these problems based on two ideas: (i) we use together a set of suitable augmentation and a consistency loss to guide the model to learn semantic representations that are invariant to the photometric and geometric shifts typical of the top-down perspective (Augmentation Invariance); (ii) we use a sampling method (Adaptive Sampling) that selects the training images based on a measure of pixel-wise distribution of classes and actual network confidence. With an extensive set of experiments conducted on the Agriculture-Vision dataset, we demonstrate that our proposed strategies improve the performance of the current state-of-the-art method.