Segmentation of urban impervious surface using cellular neural networks

Abstract

In this paper an automatic segmentation technique for endmember detection to urban impervious surface with the help of the Biophysical Composition Index (BCI) and the segmentation based on Cellular Neural Network (CNN) was proposed. In particular, we focused in the derivation of BCI through of Landsat-8 Operational Land Imager (OLI) images, to proceed to the CNN segmentation through the threshold auto-select for impervious surface estimation as a linear decision given by a linear activation function. After some simulations based on the proposed technique, the obtained results, traditional single threshold-based segmentation and Otsu algorithm are assessed in terms of accuracy achieved through a stratified sample taken of a Very High Resolution image (VHR) of WorldView-2 (WV-2) with the same date as Landsat-8 OLI. The accuracy assessment from a stratified random sample showed that the CNN segmentation was the most accurate method followed by the traditional single threshold-based segmentation.