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ABSTRACT:Remote sensing is one of the most reliable ways to monitor land use and land cover change of large areas. On the other hand, satellite images from different agencies are becoming accessible due to the new user dissemination policies. For that reason, interpretation of remotely sensed data in a spatiotemporal context is becoming a valuable research topic. In the present day, a map of change has a great significant for scientific purposes or planning and management applications. However, it is difficult to extract useful visual information from the large collection of available satellite images. For that reason, automatic or semi-automatic exploration is needed. One of the key stages in the change detection methods is threshold selection. This threshold determination problem has been addressed by several recent techniques based on Change Vector Analysis (CVA). Thus, this work provides a simple semi-automatic procedure that defines the change/no change condition and a comparative study will be involved together with the previous existing method called Double Flexible Pace Search (DFPS). This study uses Landsat Thematic Mapper scenes acquired on different dates in an Algerian region. First, some training data sets containing all possible classes of change are required and their respective supervised posterior probability maps for each scene are obtained. The selected supervised classifier is based on the Maximum Likelihood method. Then four training sets (two sets from each date) are chosen from their corresponding probability maps based on their spatial location in the original images. The optimal average will be obtained as an average of the thresholds obtained at every set. This work verifies that the proposed approach is effective on the selected area, providing improved change map results.

ABSTRACT: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. Several techniques have been used for that purpose and change vector analysis (CVA) has been frequently employed to carry out automatic spatiotemporal information extraction. This work describes a modified methodology based on Supervised Change Vector Analysis in Posterior probability Space (SCVAPS) with the final aim of obtaining a change detection map in Blida, Algeria. The proposed technique is a Modified version of Supervised Change Vector Analysis Posterior probability Space (MSCVAPS) and it is applied at the same region that the original technique studied in the literature. The classical Maximum Likelihood classifier is the selected method for supervised classification since it provides good properties in the posterior probability map. An improved method for threshold determination based on Double Flexible Pace Search (DFPS) is proposed in this work and it is employed to obtain the most adequate threshold value. Then, the MSCVAPS approach is evaluated by two cases study of the land cover change detection in the region of Blida, Algeria, and in the region of Shunyi District, Beijing, China, using a pair of Landsat Thematic Mapper images and pair of Landsat Enhanced Thematic Mapper images, respectively. The final evaluation is given by the overall accuracy of changed and unchanged pixels and the kappa coefficient. The results show that the modified approach gives excellent results using the same area of study that was selected in the literature.