RT info:eu-repo/semantics/article
T1 A spatial contextual postclassification method for preserving linear objects in multispectral imagery
A1 Rodríguez Cuenca, Borja
A1 Malpica Velasco, José A.
A1 Alonso Rodríguez, María Concepción
K1 Classification smoothing
K1 contextual classification
K1 Relaxation methods,
K1 Remote sensing
K1 Ciencia
K1 Matemáticas
AB Classification of remote sensing multispectral data is important for  segmenting images and thematic mapping and is generally the first step  in feature extraction. Per-pixel classification, based on spectral  information alone, generally produces noisy classification results. The  introduction of spatial information has been shown to be beneficial in  removing most of this noise. Probabilistic label relaxation (PLR) has  proved to be advantageous using second-order statistics; here, we  present a modified contextual probabilistic relaxation method based on  imposing directional information in the joint probability with  third-order statistics. The proposed method was tested in synthetic  images and real images; the results are compared with a "Majority"  algorithm and the classical PLR method. The proposed third-order method  gives the best results, both visually and numerically.
SN 0196-2892
YR 2013
FD 2013-01-01
LK http://hdl.handle.net/10017/32106
UL http://hdl.handle.net/10017/32106
LA eng
NO Ministerio de Ciencia e Innovación
DS MINDS@UW
RD 19-abr-2024