Tree species detection using hyperspectral and Lidar data: A novel self-supervised learning approach

Shaheen L., Rasheed B., Mazzara M.
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Accurate tree identification is essential for ecological monitoring, biodiversity assessment, and forest management. Traditional manual survey methods are labor-intensive and ineffective over large areas. Advances in remote sensing technologies including lidar and hyperspectral imaging improve automated, exact detection in many fields.

Nevertheless, these technologies typically require extensive labeled data and manual feature engineering, which restrict scalability. This research proposes a new method of Self-Supervised Learning (SSL) with the SimCLR framework to enhance the classification of tree species using unlabelled data. SSL model automatically discovers strong features by merging the spectral data from hyperspectral data with the structural data from LiDAR, eliminating the need for manual intervention.

We evaluate the performance of the SSL model against traditional classifiers, including Random Forest (RF), Support Vector Machines (SVM), and Supervised Learning methods, using a dataset from the ECODSE competition, which comprises both labeled and unlabeled samples of tree species in Florida’s Ordway-Swisher Biological Station. The SSL method has been demonstrated to be significantly more effective than traditional methods, with a validation accuracy of 97.5% compared to 95.56% for Semi-SSL and 95.03% for CNN in Supervised Learning.

Subsampling experiments showed that the SSL technique is still effective with less labeled data, with the model achieving good accuracy even with only 20% labeled data points. This conclusion demonstrates SSL’s practical applications in circumstances with insufficient labeled data, such as large-scale forest monitoring.

Keywords: self-supervised learning, tree species detection, SimCLR, hyperspectral imagery, lidar data
Citation in English: Shaheen L., Rasheed B., Mazzara M. Tree species detection using hyperspectral and Lidar data: A novel self-supervised learning approach // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1747-1763
Citation in English: Shaheen L., Rasheed B., Mazzara M. Tree species detection using hyperspectral and Lidar data: A novel self-supervised learning approach // Computer Research and Modeling, 2024, vol. 16, no. 7, pp. 1747-1763
DOI: 10.20537/2076-7633-2024-16-7-1747-1763
URL: http://crm-en.ics.org.ru/journal/article/3566/
Creative Commons License This work is licensed under a Creative Commons Attribution-NoDerivs 3.0 Unported License.

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