Supervised Optimal Scale Parameter Estimation for Multiscale Object-Based Landcover Classification

Abstract

Scale parameter selection is a key step in an object-based image analysis (OBIA) work. In existing works, the first step is the selection of optimal scale parameter, followed by feature description and later analysis. However, only low-level image features are used at this step, which are not directly related to the purpose of the application. To overcome the limitation, we propose a multiscale object-based image analysis framework, in which, the multiscale classification is performed first, and the optimal scale parameter is estimated using the multiscale classification results and training samples. The experiments have demonstrated the effectiveness of our approach in estimating optimal scale parameter for object-based landcover classification, and showed great potential in automatic analysis of high spatial resolution remote sensing images.