Automatische 3D-Veränderungsanalyse in städtischen Gebieten durch die Kombination von Höhen und Form Information

Chaabouni-Chouayakh, Houda; Reinartz, Peter

doi:10.1127/1432-8364/2011/0083

Cited by 23 publications

(12 citation statements)

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“…The idea of using Digital Surface Model (DSM) (height information) for remote sensing interpretation has recently been popularized by the advanced development of a dense matching algorithm, which produced relatively reliable DSM from photogrammetric images. Quite a few research works were devoted to using the image-derived 3D information for change detection in 3D [26][27][28], which provided more reliable results. However, integrating DSM for remote sensing data classification has not been fully investigated.…”

Section: Related Work In Classification and Data Interpretation Usinmentioning

confidence: 99%

Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile

Zhang

Qin²,

Huang

et al. 2015

Remote Sensing

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New aerial sensors and platforms (e.g., unmanned aerial vehicles (UAVs)) are capable of providing ultra-high resolution remote sensing data (less than a 30-cm ground sampling distance (GSD)). This type of data is an important source for interpreting sub-building level objects; however, it has not yet been explored. The large-scale differences of urban objects, the high spectral variability and the large perspective effect bring difficulties to the design of descriptive features. Therefore, features representing the spatial information of the objects are essential for dealing with the spectral ambiguity. In this paper, we proposed a dual morphology top-hat profile (DMTHP) using both morphology reconstruction and erosion with different granularities. Due to the high dimensional feature space, we have proposed an adaptive scale selection procedure to reduce the feature dimension according to the training samples. The DMTHP is extracted from both images and Digital Surface Models (DSM) to obtain complimentary information. The random forest classifier is used to classify the features hierarchically. Quantitative experimental results on aerial images with 9-cm and UAV images with 5-cm GSD are performed. Under our experiments, improvements of 10% and 2% in overall accuracy are obtained in comparison with the well-known differential morphological profile (DMP) feature, and superior performance is observed over other tested features. Large format data with 20,000 × 20,000 pixels are used to perform a qualitative experiment using the proposed method, which shows its promising potential. The experiments also demonstrate that the DSM information has greatly enhanced the classification accuracy. In the best case in our experiment, it gives rise to a classification accuracy from 63.93% (spectral information only) to 94.48% (the proposed method).

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Section: Related Work In Classification and Data Interpretation Usinmentioning

confidence: 99%

Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile

Zhang

Qin²,

Huang

et al. 2015

Remote Sensing

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“…Among the change detection techniques using VHR images for building detection, most of the methods first focus on the interpretation of the height and textural difference, and then apply post-filtering techniques to eliminate unwanted changes (Chaabouni-Chouayakh and Reinartz, 2011;Rottensteiner, 2008). The performance of the change detection methods rely largely on the quality of the images and DSMs in the first place, meanwhile it is also crucial to find a good way to distinguish the unwanted changes (blunders from DSMs or seasonal variation of the vegetation).…”

Section: Introductionmentioning

confidence: 99%

A supervised method for object-based 3D building change detection on aerial stereo images

Ran¹,

Gruen²

2014

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

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ABSTRACT:There is a great demand for studying the changes of buildings over time. The current trend for building change detection combines the orthophoto and DSM (Digital Surface Models). The pixel-based change detection methods are very sensitive to the quality of the images and DSMs, while the object-based methods are more robust towards these problems. In this paper, we propose a supervised method for building change detection. After a segment-based SVM (Support Vector Machine) classification with features extracted from the orthophoto and DSM, we focus on the detection of the building changes of different periods by measuring their height and texture differences, as well as their shapes. A decision tree analysis is used to assess the probability of change for each building segment and the traffic lighting system is used to indicate the status "change", "non-change" and "uncertain change" for building segments. The proposed method is applied to scanned aerial photos of the city of Zurich in 2002 and 2007, and the results have demonstrated that our method is able to achieve high detection accuracy.

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“…Similar approaches can be found in [19,20]. Since simple subtraction of height values may lead to false positives and noisy results, Chaabouni-Chouayakh and Reinartz [21] applied a post-classification on an initial change mask computed by DSM subtraction, taking into account various shape features such as elongation, eccentricity and solidity. Similar methods were proposed in [8,22], where the classification was performed on the aerial images.…”

Section: Related Workmentioning

confidence: 99%

“…Among the proposed change detection methods, the most prominent trend is to combine both the height information and spectral information [21,23,25]. Especially in the case of very high resolution, the spectral information creates high intra-class variability and luminance discrepancy, so that change detection methods with spectral information alone bring a large number of false positives.…”

Section: Change Detectionmentioning

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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Automatische 3D-Veränderungsanalyse in städtischen Gebieten durch die Kombination von Höhen und Form Information

Cited by 23 publications

References 0 publications

Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile

Classification of Ultra-High Resolution Orthophotos Combined with DSM Using a Dual Morphological Top Hat Profile

A supervised method for object-based 3D building change detection on aerial stereo images

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

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