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DOREEN BOYD's Outputs (3)

Supervised methods of image segmentation accuracy assessment in land cover mapping (2017)
Journal Article
Costa, H., Foody, G. M., & Boyd, D. S. (2018). Supervised methods of image segmentation accuracy assessment in land cover mapping. Remote Sensing of Environment, 205, https://doi.org/10.1016/j.rse.2017.11.024

Land cover mapping via image classification is sometimes realized through object-based image analysis. Objects are typically constructed by partitioning imagery into spatially contiguous groups of pixels through image segmentation and used as the bas... Read More about Supervised methods of image segmentation accuracy assessment in land cover mapping.

Identifying species from the air: UAVs and the very high resolution challenge for plant conservation (2017)
Journal Article
Baena, S., Moat, J., Whaley, O., & Boyd, D. S. (2017). Identifying species from the air: UAVs and the very high resolution challenge for plant conservation. PLoS ONE, 12(11), Article e0188714. https://doi.org/10.1371/journal.pone.0188714

The Pacific Equatorial dry forest of Northern Peru is recognised for its unique endemic biodiversity. Although highly threatened the forest provides livelihoods and ecosystem services to local communities. As agro-industrial expansion and climatic va... Read More about Identifying species from the air: UAVs and the very high resolution challenge for plant conservation.

Using mixed objects in the training of object-based image classifications (2017)
Journal Article
Costa, H., Foody, G. M., & Boyd, D. S. (2017). Using mixed objects in the training of object-based image classifications. Remote Sensing of Environment, 190, https://doi.org/10.1016/j.rse.2016.12.017

Image classification for thematic mapping is a very common application in remote sensing, which is sometimes realized through object-based image analysis. In these analyses, it is common for some of the objects to be mixed in their class composition... Read More about Using mixed objects in the training of object-based image classifications.