Abstract
In this work, we propose an integrative framework to process UAV images. The overall process can be viewed as a pipeline consisting of the geometric and radiometric corrections, subsequent panoramic mosaicking and hierarchical image segmentation for later Object Based Image Analysis (OBIA). More precisely, we first introduce an efficient image stitching algorithm after the geometric calibration and radiometric correction, which employs a fast feature extraction and matching by combining the local difference binary descriptor and the local sensitive hashing. We then use a Binary Partition Tree (BPT) representation for the large mosaicked panoramic image, which starts by the definition of an initial partition obtained by an over-segmentation algorithm, i.e., the simple linear iterative clustering (SLIC). Finally, we build an object-based hierarchical structure by fully considering the spectral and spatial information of the super-pixels and their topological relationships. Moreover, an optimal segmentation is obtained by filtering the complex hierarchies into simpler ones according to some criterions, such as the uniform homogeneity and semantic consistency. Experimental results on processing the post-seismic UAV images of the 2013 Ya’an earthquake demonstrate the effectiveness and efficiency of our proposed method.
Highlights
1.1 Motivation and ObjectiveNowadays, Unmanned Aerial Vehicles (UAVs)-based imaging systems have been applied in many remote sensing applications, such as agriculture and forestry, natural disasters and environmental issues
With the rapid development of technique, UAV systems are equipped with high accurate inertial navigation system, which can be used to speed up the image registration
In the low altitude UAV-based imaging system, image distortion correction is essential for promoting the quality of the final panoramic image
Summary
1.1 Motivation and ObjectiveNowadays, Unmanned Aerial Vehicles (UAVs)-based imaging systems have been applied in many remote sensing applications, such as agriculture and forestry, natural disasters and environmental issues. A Unmanned Aerial System (UAS)-based image acquisition commonly results in hundreds of very high resolution, small footprint images, which pose great challenges for subsequent applications. A simple solution to this problem is increasing the UAV flight altitude a single image can cover larger area and the total amount of image pieces can be decreased. It is inapplicable since civilian UAV can only fly at limited altitude. The stitching output is a large scale panorama with very high resolution (VHR). The segmentation and classification of large scale VHR panorama is the important tasks for processing UAV image. The interpretation of very large scale images remains a great challenge for the big data volume and semantic complexity
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