Enhancement of the double flexible pace search threshold determination for change vector analysis
- 1Instituto de Telecomunicaciones y Aplicaciones Multimedia (iTEAM), Universitat Politècnica de València, Valencia, Spain
- 2Component, signal and systems laboratory, University Abdelhamid Ibn Badis of Mostaganem, Mostaganem, Algeria
Keywords: Threshold determination, Change Detection, Change Vector Analysis (CVA) , Supervised Classification, Land Cover Land Change
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.