SOENet: a multi-resolution network for sheep extraction from high-resolution remote sensing images

Abstract

Sheep are a primary species in animal husbandry. Accurate sheep population counts are vital for managing husbandry practices and preventing grassland overgrazing. Current methods using UAV images are time-consuming and costly. The small size of sheep and complex backgrounds in large-scale areas make accurate extraction challenging. We propose the Small Object Extraction Net (SOENet), a semantic segmentation model with an encoder–decoder structure. The SOENet encoder resizes the original images to three resolutions to extract multi-scale sheep features. A Multi-resolution Context Enhancement (MCE) module is proposed to extract complicated contextual features of sheep by concatenating different scales of feature maps to reduce the possibility of commission errors. To minimize omission errors, a Multi-resolution Feature Fusion (MFF) module is proposed by introducing more scales of features to the encoder, facilitating the sharing and exchange of multi-scale sheep features. Our model outperforms nine deep learning models, with a 7.9% improvement in precision and a 1.2% increase in mIoU. SOENet provides an effective solution for large-scale sheep extraction from various ground objects.

Keywords

• Deep learning
• semantic segmentation
• sheep extraction