Balaguer Beser, Ángel Antonio
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Balaguer Beser
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Ángel Antonio
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- PublicationDescription and validation of a new set of object-based temporal geostatistical features for land-use/land-cover change detection(Elsevier, 2016-11) Gil Yepes, José Luis; Ruiz Fernández, Luis Ángel; Recio Recio, Jorge Abel; Balaguer Beser, Ángel Antonio; Hermosilla Gómez, Txomin; Dpto. de Matemática Aplicada; Dpto. de Ingeniería Cartográfica Geodesia y Fotogrametría; Escuela Técnica Superior de Ingeniería Geodésica, Cartográfica y Topográfica; Grupo de Cartografía Geoambiental y Teledetección; Generalitat Valenciana; Comunidad Autónoma de la Región de Murcia[EN] A new set of temporal features derived from codispersion and cross-semivariogram geostatistical functions is proposed, described, extracted, and evaluated for object-based land-use/land-cover change detection using high resolution images. Five features were extracted from the codispersion function and another six from the cross-semivariogram. The set of features describes the temporal behaviour of the internal structure of the image objects defined in a cadastral database. The set of extracted features was combined with spectral information and a feature selection study was performed using forward stepwise discriminant analysis, principal component analysis, as well as correlation and feature interpretation analysis. The temporal feature set was validated using high resolution aerial images from an agricultural area located in south-east Spain, in order to solve a tree crop change detection problem. Direct classification using decision tree classifier was used as change detection method. Different classifications were performed comparing various feature group combinations in order to obtain the most suitable features for this study. Results showed that the new sets of cross-semivariogram and codispersion features provided high global accuracy classification results (83.55% and 85.71% respectively), showing high potential for detecting changes related to the internal structure of agricultural tree crop parcels. A significant increase in accuracy value was obtained when combining features from both groups with spectral information (94.59%).