Mr. Andrés C. Rodríguez Profile

Andrés C. Rodríguez

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Proceedings Article | 12 September 2021 Presentation + Paper

Deep learning for sub-pixel palm tree classification using spaceborne Sentinel-2 imagery

María Culman, Andrés Rodríguez, Jan Dirk Wegner, Stephanie Delalieux, Ben Somers

Proceedings Volume 11856, 118560E (2021) https://doi.org/10.1117/12.2599861

KEYWORDS: RGB color model, Remote sensing, Image resolution, Image segmentation, Vegetation, Image classification, Satellites, Orthophoto maps

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The challenge of classifying and locating Phoenix palm trees in different scenes with different appearances and varied ages has been addressed with deep learning object detection over aerial images. Nevertheless, an explicit limitation hereof is that palms should be visually identifiable in the image—i.e., palm crowns should be larger than the pixel size. Unfortunately, high-spatial resolution imagery is not widely and directly available in the Phoenix palm growing regions of the Mediterranean, Middle East, and North Africa. This study, therefore, presents the re-implementation of a semantic segmentation architecture to train a model able to classify Phoenix palm pixels. This is applied to freely available medium resolution space-borne Sentinel-2 images over the Spanish island of La Gomera (Canary Islands). At the study site, a total of 116,330 Phoenix palms had been inventoried by the local government. Palms appear in multiple, heterogeneous environments, which implies a background variability that is a persistent challenge for palm pixel classification. The re-implemented architecture is a novelty in deep semantic segmentation and density estimation initially developed for counting objects of sub-pixel size. And it proved to be successful for creating a model of palm classification, thereby compensating for the limited spatial resolution of the Sentinel-2 images. The palm tree sub-pixel classification model achieved an overall accuracy of 0.921, with a recall and precision of 0.438 and 0.522. These results demonstrate the potential of remote sensing data of medium-spatial resolution for vegetation mapping in applications where trees are scattered over extensive areas.

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