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Remote Sens. 2014, 6(9), 7911-7932; doi:10.3390/rs6097911

An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images

Singapore-ETH Centre, Future Cities Laboratory, 1 CREATE Way, #06-01 CREATE Tower, 138602, Singapore
Received: 9 June 2014 / Revised: 7 August 2014 / Accepted: 7 August 2014 / Published: 25 August 2014
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Abstract

There have been increasing demands for automatically monitoring urban areas in very high detail, and the Unmanned Aerial Vehicle (UAV) with auto-navigation (AUNA) system offers such capability. This study proposes an object-based hierarchical method to detect changes from UAV images taken at different times. It consists of several steps. In the first step, an octocopter with AUNA capability is used to acquire images at different dates. These images are registered automatically, based on SIFT (Scale-Invariant Feature Transform) feature points, via the general bundle adjustment framework. Thus, the Digital Surface Models (DSMs) and orthophotos can be generated for raster-based change analysis. In the next step, a multi-primitive segmentation method combining the spectral and geometric information is proposed for object-based analysis. In the final step, a multi-criteria decision analysis is carried out concerning the height, spectral and geometric coherence, and shape regularity for change determination. Experiments based on UAV images with five-centimeter ground resolution demonstrate the effectiveness of the proposed method, leading to the conclusion that this method is practically applicable for frequent monitoring. View Full-Text
Keywords: change detection; unmanned aerial vehicle; digital surface models; multi-criteria decision; semi-global matching change detection; unmanned aerial vehicle; digital surface models; multi-criteria decision; semi-global matching
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This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Qin, R. An Object-Based Hierarchical Method for Change Detection Using Unmanned Aerial Vehicle Images. Remote Sens. 2014, 6, 7911-7932.

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