@article {Malinverni20111025,
	title = {Hybrid object-based approach for land use/land cover mapping using high spatial resolution imagery},
	journal = {International Journal of Geographical Information Science},
	volume = {25},
	number = {6},
	year = {2011},
	note = {cited By 13},
	pages = {1025-1043},
	abstract = {Traditionally, remote sensing has employed pixel-based classification techniques to deal with land use/land cover (LULC) studies. Generally, pixel-based approaches have been proven to work well with low spatial resolution imagery (e.g. Landsat or System Pour L{\textquoteright}Observation de la Terre sensors). Now, however, commercially available high spatial resolution images (e.g. aerial Leica ADS40 and Vexcel UltraCam sensors, and satellite IKONOS, Quickbird, GeoEye and WorldView sensors) can be problematic for pixel-based analysis due to their tendency to oversample the scene. This is driving research towards object-based approaches. This article proposes a hybrid classification method with the aim of incorporating the advantages of supervised pixel-based classification into object-based approaches. The method has been developed for medium- scale (1:10,000) LULC mapping using ADS40 imagery with 1 m ground sampling distance. First, spatial information is incorporated into a pixel-based classification (AdaBoost classifier) by means of additional texture features (Haralick, Gabor, Law features), which can be selected {\textquoteright}ad hoc{\textquoteright} according to optimal training samples ({\textquoteright}Relief-F{\textquoteright} pproach,Mahalanobis distances). Then a rule-based approach sorts segmented regions into thematic CORINE Land Cover classes in terms of membership class percentages (a modified Winner-Takes-All approach) and shape parameters. Finally, ancillary data (roads, rivers, etc.) are exploited to increase classification accuracy. The experimental results show that the proposed hybrid approach allows the extraction of more LULC classes than conventional pixel-based methods, while improving classification accuracy considerably. A second contribution of this article is the assessment of classification reliability by implementing a stability map, in addition to confusion matrices. {\textcopyright} 2011 Taylor \& Francis.},
	doi = {10.1080/13658816.2011.566569},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-79960685342\&partnerID=40\&md5=18767c8a88bf2abff53ef96ec138f0aa},
	author = {Eva Savina Malinverni and Anna Nora Tassetti and Adriano Mancini and Primo Zingaretti and Emanuele Frontoni and A. Bernardini}
}
@article {Zingaretti2009500,
	title = {A hybrid approach to land cover classification from multi spectral images},
	journal = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
	volume = {5716 LNCS},
	year = {2009},
	note = {cited By 5},
	pages = {500-508},
	abstract = {This work is part of a wider project whose general objective is to develop a methodology for the automatic classification, based on CORINE land-cover (CLC) classes, of high resolution multispectral IKONOS images. The specific objective of this paper is to describe a new methodology for producing really exploitable results from automatic classification algorithms. Input data are basically constituted by multispectral images, integrated with textural and contextual measures. The output is constituted by an image with each pixel assigned to one out of 15 classes at the second level of the CLC legend or let unclassified (somehow a better solution than a classification error), plus a stability map that helps users to separate the regions classified with high accuracy from those whose classification result should be verified before being used. {\textcopyright} 2009 Springer Berlin Heidelberg.},
	doi = {10.1007/978-3-642-04146-4_54},
	url = {http://www.scopus.com/inward/record.url?eid=2-s2.0-76249123810\&partnerID=40\&md5=fcaeb5e46615d9bbdf96fcbf4ffac41a},
	author = {Primo Zingaretti and Emanuele Frontoni and Eva Savina Malinverni and Adriano Mancini}
}
