Topographical Change Detection from UAV Imagery Using M-DSM Method

Xuan, Wei

doi:10.1007/978-3-642-23223-7_77

Cited by 4 publications

(2 citation statements)

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“…However, this method was essentially an image-based method, which did not employ the geometric information. Xuan [29] used a UAV with AUNA capability and adopted a simple subtraction on manually edited DSMs for change detection. The images between two dates were registered by ground control points (GCP).…”

Section: Related Workmentioning

confidence: 99%

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

Qin¹

2014

Remote Sensing

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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.

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Section: Related Workmentioning

confidence: 99%

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

Qin¹

2014

Remote Sensing

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“…The use of UAVs for the acquisition of very high resolution images has become a common platform in the geomatics field (Nex & Remondino, 2014), and proven to be good for urban area change detection up to the building level (Qin, 2014). When comparing to the past airborne sensors, UAVs have the same advantages such as the possibility of acquiring data in a small area at an affordable cost and they require lower costs in recruitment of staff as explained by Xuan (2011) and hardware (though they require a certified pilot in most of the countries! ).…”

Section: Introductionmentioning

confidence: 99%

Multi-Temporal Classification and Change Detection Using Uav Images

Makuti¹,

Nex²,

Yang³

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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In this paper different methodologies for the classification and change detection of UAV image blocks are explored. UAV is not only the cheapest platform for image acquisition but it is also the easiest platform to operate in repeated data collections over a changing area like a building construction site. Two change detection techniques have been evaluated in this study: the pre-classification and the post-classification algorithms. These methods are based on three main steps: feature extraction, classification and change detection. A set of state of the art features have been used in the tests: colour features (HSV), textural features (GLCM) and 3D geometric features. For classification purposes Conditional Random Field (CRF) has been used: the unary potential was determined using the Random Forest algorithm while the pairwise potential was defined by the fully connected CRF. In the performed tests, different feature configurations and settings have been considered to assess the performance of these methods in such challenging task. Experimental results showed that the post-classification approach outperforms the pre-classification change detection method. This was analysed using the overall accuracy, where by post classification have an accuracy of up to 62.6% and the pre classification change detection have an accuracy of 46.5%. These results represent a first useful indication for future works and developments.Orthophoto DSM Figure 1: orthophoto and DSM for epoch one.

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Detecting new building construction in urban areas based on images of small unmanned aerial system

Ning

Huang

et al. 2019

Papers in Applied Geography

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Topographical Change Detection from UAV Imagery Using M-DSM Method

Cited by 4 publications

References 1 publication

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

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

Multi-Temporal Classification and Change Detection Using Uav Images

Detecting new building construction in urban areas based on images of small unmanned aerial system

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