Satellite image segmentation using graph representation and morphological processing

Abstract

The segmentation process of satellite imagery becomes currently a significant step in remote sensing with the arrival of very high spatial resolution images. Indeed, the arrival of these images enables a new capability to study a range of non-observable objects until now. Using high-resolution imagery should make it possible to detect man made features (such as buildings and roads) or detailed components of vegetation (such as trees or heterogeneous woodlands) in an easier way than conventional data. In this paper, we present a brief review of segmentation techniques, the principal advances in earth observation technology, and the evolution of the high-resolution technology. Also, we present a self-adapting method of segmentation of very high-resolution satellite images. This approach is based on a description of the image using graphs of adjacency and morphological processing to obtain suitable and significant computed components by the growth of regions. Finally we present some examples of the segmentation and the feature extraction done in some high-resolution images.