Land-cover classification with an expert classification algorithm using digital aerial photographs - Perea, Alberto J.; Meroño, José E.; Aguilera, María J.; De la Cruz, José L. (South African Journal of Science, 2010)
The purpose of this study was to evaluate the usefulness of the spectral information of digital aerial sensors in determining land-cover classification using new digital techniques. The land covers that have been evaluated are the following, (1) bare soil, (2) cereals, including maize (Zea mays L.), oats (Avena sativa L.), rye (Secale cereale L.), wheat (Triticum aestivum L.) and barley (Hordeun vulgare L.), (3) high protein crops, such as peas (Pisum sativum L.) and beans (Vicia faba L.), (4) alfalfa (Medicago sativa L.), (5) woodlands and scrublands, including holly oak (Quercus ilex L.) and common retama (Retama sphaerocarpa L.), (6) urban soil, (7) olive groves (Olea europaea L.) and (8) burnt crop stubble. The best result was obtained using an expert classification algorithm, achieving a reliability rate of 95%. This result showed that the images of digital airborne sensors hold considerable promise for the future in the field of digital classifications because these images contain valuable information that takes advantage of the geometric viewpoint. Moreover, new classification techniques reduce problems encountered using high-resolution images; while reliabilities are achieved that are better than those achieved with traditional methods.
Keywords: Algorithm; digital aerial photography; expert classification; land-cover classification; object- oriented classification; UltracamD

O Algoritmo BARCOR: Classificação de Cortiça para Rolhas Recorrendo a Quatro Atributos de Qualidade - Barreto, Luís Soares (Silva Lusitana, 2008)
t. The author proposes an analytical method named algorithm BARCOR that integrates TOPSIS ("Technique for Order Preference by Similarity to Ideal Solution"), regression, tree models and cluster analysis, to select the minimum number of attributes of cork quality to efficiently classify this material when used to the production of stoppers. When using attributes evenly weighted he shows that only the four attributes are sufficient. If unevenly weights of the attributes are used, this number can be reduced to three. He proposes two models with four and three attributes as explanatory variables to obtain expedite and preliminary classifications of the cork. He admits that the algorithm has potential to be applied in other similar situations.
The following values have no corresponding Zotero field: publisher: scielopt
Keywords: Algorithm; Classification; Cork; Quality; stoppers