@article {Bernardini201043,
	title = {Pixel, object and hybrid classification comparisons},
	journal = {Journal of Spatial Science},
	volume = {55},
	number = {1},
	year = {2010},
	note = {cited By 3},
	pages = {43-54},
	abstract = {The choice of the best classification approach for thematic map generation relies on many factors, such as image resolution and minimum mapping unit. The generalized GIS-ready products derived from the results of pixel-based approaches and the availability of higherresolution imagery have directed research towards object-based classification approaches. In this paper we present the superior performance of a hybrid methodology that combines the results of automatic segmentation with the land cover information derived from a pixel classification by means of the Winner Takes All (WTA) algorithm. Land use and land cover results obtained through this hybrid classification approach are compared with those of a One Against All (OAA) object-oriented classification approach. {\textcopyright} 2010 Surveying and Spatial Sciences Institute and Mapping Sciences Institute, Australia.},
	doi = {10.1080/14498596.2010.487641},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-80052137947\&partnerID=40\&md5=74b7a4fe82987b5213c27b7315b0f768},
	author = {A. Bernardini and Emanuele Frontoni and Eva Savina Malinverni and Adriano Mancini and Anna Nora Tassetti and Primo Zingaretti}
}
@conference {Frontoni2009,
	title = {Stability maps for really exploitable automatic classification results},
	booktitle = {2009 17th International Conference on Geoinformatics, Geoinformatics 2009},
	year = {2009},
	note = {cited By 1},
	abstract = {The paper describes a new methodology for producing really exploitable results from automatic classification algorithms. The output of these algorithms is usually constituted by an image with each region assigned to one out of n classes. If the end user, on the basis of results obtained from a control set provided with a ground truth, simply knows that classification over the whole dataset can be considered correct at, for example, 85\% (s)he cannot know where correct and erroneously classified regions are really located in the whole dataset. Obviously, the result obtained can be exploited to effectively compute global indexes over the dataset, but it cannot be used as a thematic map. Thus, in addition to the assignment of a class to each region we propose an approach that provides a stability map, a binary image that separates regions (S) classified with high accuracy from those (U) whose classification result should be verified before being used. Two further benefits derive from the construction of the stability map: the control set can be used to set up a good threshold for binarizing the stability map (that is, a threshold by which all regions S are effectively correctly classified); unreliable regions U can help the end user to identify principal causes of (types of regions leading to) misclassification and corresponding (fuzzy, neural, rule based, etc.) approaches to overcome them.},
	doi = {10.1109/GEOINFORMATICS.2009.5293443},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-74349085598\&partnerID=40\&md5=9cde6053a2e8199444209988fccd5e2c},
	author = {Emanuele Frontoni and A. Bernardini and Eva Savina Malinverni and Adriano Mancini and Primo Zingaretti}
}
