Augmentation Invariance and Adaptive Sampling in Semantic Segmentation
of Agricultural Aerial Images
- URL: http://arxiv.org/abs/2204.07969v1
- Date: Sun, 17 Apr 2022 10:19:07 GMT
- Title: Augmentation Invariance and Adaptive Sampling in Semantic Segmentation
of Agricultural Aerial Images
- Authors: Antonio Tavera, Edoardo Arnaudo, Carlo Masone, Barbara Caputo
- Abstract summary: We investigate the problem of Semantic for agricultural aerial imagery.
The existing methods used for this task are designed without considering two characteristics of the aerial data.
We propose a solution 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.
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.
- Score: 16.101248613062292
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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