Extraction of salt-marsh vegetation “fairy circles” from UAV images by the combination of SAM visual segmentation model and random forest machine learning algorithm

Zhou Ruotong; Tan Kai; Yang Jianru; Han Jiangtao; Zhang Weiguo

doi:10.12284/hyxb2024048

Volume 46 Issue 5

May 2024

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Article Navigation > Haiyang Xuebao > 2024 > 46(5): 116-126

Zhou Ruotong，Tan Kai，Yang Jianru, et al. Extraction of salt-marsh vegetation “fairy circles” from UAV images by the combination of SAM visual segmentation model and random forest machine learning algorithm[J]. Haiyang Xuebao，2024, 46(5)：116–126 doi: 10.12284/hyxb2024048

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Extraction of salt-marsh vegetation “fairy circles” from UAV images by the combination of SAM visual segmentation model and random forest machine learning algorithm

doi: 10.12284/hyxb2024048

State Key Laboratory of Estuarine and Coastal Research, East China Normal University, Shanghai 200241, China

Received Date: 2023-09-29
Accepted Date: 2024-03-01
Rev Recd Date: 2023-12-28

Available Online: 2024-03-11

Publish Date: 2024-05-01

Abstract

Abstract

The “fairy circle” represents a unique form of spatial self-organization found within coastal salt marsh ecosystems, profoundly influencing the productivity, stability, and resilience of these wetlands. Unmanned Aerial Vehicle (UAV) imagery plays a pivotal role in precisely pinpointing the “fairy circle” locations and deciphering their temporal and spatial development trends. However, identifying “fairy circle” pixels within two-dimensional images poses a considerable technical challenge due to the subtle differences in color and shape characteristics between these pixels and their surroundings. Therefore, intelligently and accurately identify “fairy circle” pixels from two-dimensional images and form individual “fairy circle” for the identified pixels were the current technical difficulties. This paper introduced an innovative approach to extract “fairy circle” from UAV images by integrating the SAM (Segment Anything Model) visual segmentation model with random forest machine learning. This novel method accomplished the recognition and extraction of individual “fairy circle” through a two-step process: segmentation followed by classification. Initially, we established Dice (Sørensen-Dice coefficient) and IOU (Intersection Over Union) evaluation metrics, and optimize SAM’s pre-trained model parameters, which produced segmentation mask devoid of attribute information by fully automated image segmentation. Subsequently, we aligned the segmentation mask with the original image, and utilized RGB (red, green, and blue) color channels and spatial coordinates to construct a feature index for the segmentation mask. These features underwent analysis and selection based on Out-of-Bag (OOB) error reduction and feature distribution patterns. Ultimately, the refined features were employed to train a random forest model, enabling the automatic identification and classification of “fairy circle” vegetation, common vegetation, and bare flat areas. The experimental results show that the average correct extraction rate of “fairy circle” is 96.1%, and the average wrong extraction rate is 9.5%, which provides methodological and technological support for the accurate depiction of the spatial and temporal pattern of “fairy circle” as well as the processing of coastal remote sensing images by UAVs.
- salt marsh vegetation,
- fairy circle,
- segment anything model (SAM),
- unmanned Aerial Vehicle images,
- machine learning

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References

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