TWO-STAGE SEGMENTATION OF AERIAL IMAGES FOR SEARCH AND RESCUE

Abstract

This paper presents a novel two-stage image segmentation approach for detection of artificial materials and objects in non-urban terrain. It is based on the assumption that an object and a natural background have different color/saturation. High resolution image of the unknown terrain taken with digital camera from a distance of about 100 m is divided into smaller sub-images for further processing. Each sub-image is clustered using mean shift algorithm, and information about obtained cluster centers is transferred to the next stage. Second stage uses information about cluster centers from all sub-images and applies the same clustering method to this data set. Finally, a decision-making module evaluates all clusters and eventually proposes image segments that have high possibility of presenting the artificial material or the object in the input image. The proposed method has been tested on 22 aerial images typical for search and rescue. Observed results are validated through recall and precision and it has been shown that the proposed method can be successfully used for real search and rescue operations.