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Abstract

The need for understanding the terrain or conditions of large areas aerially has gained prominence as the aerial images provide a near clear coverage of the area under study. Individual image provides just a portion of the area, thus to understand the whole area, mosaicking or stitching of these images is needed. Image mosaicking aids in providing with a ”Big Picture” as an outcome by joining the images taken during the flight. In this paper we propose a method which aims at generating a seamless aerial mosaick using only the images captured by the UAV as input. This involves identifying candidate images from the images captured by the UAV periodically during its flight and stitching the images together. This method evaluates various feature descriptors and feature matching techniques that can be integrated into the mosaicking system. The proposed work is a hybrid approach that uses the Scale Invariant Feature Transform (SIFT) for feature extraction and the key features are matched using the Fast Library for Approximate Nearest Neighbors (FLANN). RANdom Sample Consensus (RANSAC), is used for the removal of features that are redundant or act as outliner, providing candidates for Homography estimation. This is followed by image stitching that involves the use of Multi-Band Blending to produce a visually seamless mosaick. The results obtained were evaluated for quality using Universal Quality index Measure (QIM) and is found to be perfect.

Keywords

Mosaicking UAV SIFT FLANN RANSAC Homography Multi-band Blending

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A hybrid approach to generate visually seamless aerial mosaicks from unmanned aerial vehicles

Abstract

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