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The multispectral and multitemporal data coming from satellites allow us to extract valuable spatiotemporal change. Consequently,Earth surface change detection analysis has been used in the past to monitor land cover changes caused by different reasons. Severaltechniques have been used for that purpose and change vector analysis (CVA) has been frequently employed to carry out automaticspatiotemporal information extraction. This work describes a modified methodology based on Supervised Change Vector Analysisin Posterior probability Space (SCVAPS) with the final aim of obtaining a change detection map in Blida, Algeria. The proposedtechnique is a Modified version of Supervised Change Vector Analysis Posterior probability Space (MSCVAPS) and it is applied at thesame region that the original technique studied in the literature. The classical Maximum Likelihood classifier is the selected methodfor supervised classification since it provides good properties in the posterior probability map. An improved method for thresholddetermination based on Double Flexible Pace Search (DFPS) is proposed in this work and it is employed to obtain the most adequatethreshold value. Then, the MSCVAPS approach is evaluated by two cases study of the land cover change detection in the regionof Blida, Algeria, and in the region of Shunyi District, Beijing, China, using a pair of Landsat Thematic Mapper images and pairof Landsat Enhanced Thematic Mapper images, respectively. The final evaluation is given by the overall accuracy of changed andunchanged pixels and the kappa coefficient. The results show that the modified approach gives excellent results using the same area ofstudy that was selected in the literature

the international archives of the photogrammetry, remote sensing and spatial information sciences/international archives of the photogrammetry, remote sensing and spatial information sciences

A modified approach for change detection using change vector analysis in posterior probability space