The sensitivity of mapping methods to reference data quality: training supervised image classifications with imperfect reference data
(2016)
Journal Article
Foody, G. M., Pal, M., Rocchini, D., Garzon-Lopez, C. X., & Bastin, L. (2016). The sensitivity of mapping methods to reference data quality: training supervised image classifications with imperfect reference data. ISPRS International Journal of Geo-Information, 5(11), Article 199. https://doi.org/10.3390/ijgi5110199
The accuracy of a map is dependent on the reference dataset used in its construction. Classification analyses used in thematic mapping can, for example, be sensitive to a range of sampling and data quality concerns. With particular focus on the latte... Read More about The sensitivity of mapping methods to reference data quality: training supervised image classifications with imperfect reference data.