Small sample bare soil segmentation based on semi supervised learning

Shenlin Tang; Zhonghui Zhang; Chunlin Wang; Chengrong Pan; Hui Yang; Yanlan Wu

doi:10.1117/12.3040622

10 September 2024 Small sample bare soil segmentation based on semi supervised learning

Shenlin Tang, Zhonghui Zhang, Chunlin Wang, Chengrong Pan, Hui Yang, Yanlan Wu

Proceedings Volume 13257, International Conference on Advanced Image Processing Technology (AIPT 2024); 1325710 (2024) https://doi.org/10.1117/12.3040622
Event: International Conference on Advanced Image Processing Technology (AIPT 2024), 2024, Chongqing, China

Abstract

Accurate extraction of bare soil is crucial for land management, soil conservation, and assessment of natural disaster risks. With the continuous development of remote sensing technology, remote sensing imagery has become an important means of acquiring information about the Earth's surface. Its characteristics of multi-source data, and global coverage provide efficient methods for bare soil extraction. Although deep convolutional networks have made significant progress in image semantic segmentation, they typically require a large number of densely labeled images for training. Therefore, we propose a segmentation network called PAN-Net, which combines unsupervised learning with few-shot segmentation to address the problem of insufficient training samples for the encoder to effectively extract bare soil features. The encoder of PAN-Net is trained on a large number of unlabeled images using unsupervised learning, and the trained encoder performs well in extracting bare soil features. Finally, we apply it to downstream few-shot segmentation tasks, improving the capability of bare soil feature extraction in few-shot segmentation. To better evaluate the model performance, we validate it on our self-made dataset of over 1000 samples. Our model achieves an IoU score of 75% and an F1 score of 80% on the self-made test set, surpassing most existing methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shenlin Tang, Zhonghui Zhang, Chunlin Wang, Chengrong Pan, Hui Yang, and Yanlan Wu "Small sample bare soil segmentation based on semi supervised learning", Proc. SPIE 13257, International Conference on Advanced Image Processing Technology (AIPT 2024), 1325710 (10 September 2024); https://doi.org/10.1117/12.3040622

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KEYWORDS

Education and training

Prototyping

Data modeling

Machine learning

Image segmentation

Statistical modeling

Feature extraction

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