Automated building extraction from IKONOS images in suburban areas

Grigillo, Dejan; Fras, Mojca Kosmatin; Petrovič, Dušan

doi:10.1080/01431161.2012.659356

Cited by 19 publications

(16 citation statements)

References 28 publications

(25 reference statements)

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“…By reducing the dataset's size, we observed that the RMSE does not change. DSMs are incorporated in the process of image classification in urban/rural areas, for a holistic approach of land use/cover with improved results [21], or a targeted image classification, as is the case of urban green vegetation [3], but also in the process of the extraction of buildings [22,23], which resulted in more accurate results. The incorporation of DSMs in mapping elements in urban environments can benefit the current research by improving the results by incorporating texture and shape information apart from the spectral information [24,25].…”

Section: Resultsmentioning

confidence: 99%

Validation of Pleiades Tri-Stereo DSM in Urban Areas

Panagiotakis

Chrysoulakis

Charalampopoulou

et al. 2018

IJGI

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Abstract:We present an accurate digital surface model (DSM) derived from high-resolution Pleiades-1B 0.5 m panchromatic tri-stereo images, covering an area of 400 km 2 over the Athens Metropolitan Area. Remote sensing and photogrammetry tools were applied, resulting in a 1 m × 1 m posting DSM over the study area. The accuracy of the produced DSM was evaluated against measured elevations by a differential Global Positioning System (d-GPS) and a reference DSM provided by the National Cadaster and Mapping Agency S.A. Different combinations of stereo and tri-stereo images were used and tested on the quality of the produced DSM. Results revealed that the DSM produced by the tri-stereo analysis has a root mean square error (RMSE) of 1.17 m in elevation, which lies within the best reported in the literature. On the other hand, DSMs derived by standard analysis of stereo-pairs from the same sensor were found to perform worse. Line profile data showed similar patterns between the reference and produced DSM. Pleiades tri-stereo high-quality DSM products have the necessary accuracy to support applications in the domains of urban planning, including climate change mitigation and adaptation, hydrological modelling, and natural hazards, being an important input for simulation models and morphological analysis at local scales.

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Section: Resultsmentioning

confidence: 99%

Validation of Pleiades Tri-Stereo DSM in Urban Areas

Panagiotakis

Chrysoulakis

Charalampopoulou

et al. 2018

IJGI

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“…The building orientations are estimated using a simple new technique proposed in this article instead of building detection techniques already proposed in References [33][34][35][36][37]. Such previous techniques are not suitable to estimate the building orientations because they cannot automatically detect all buildings, which has been constantly argued in References [33,35,36]. Furthermore, the spatial resolution of post-disaster multi-looked PolSAR POAs is generally sufficient to cover multiple small buildings in one pixel; therefore, estimating dominant orientation of a group of buildings is appropriate for pre-disaster POA simulation.…”

Section: 2mentioning

confidence: 99%

“…Lines are representative features in the built-up areas; therefore, many urban applications have used them [35][36][37][38][39]. The Hough transform (HT) [40] is the common line extraction method in such applications.…”

Section: Line Extraction Using the Rghtmentioning

confidence: 99%

Comparison of Pre-Event VHR Optical Data and Post-Event PolSAR Data to Investigate Damage Caused by the 2011 Japan Tsunami in Built-Up Areas

2018

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Combining pre-disaster optical and post-disaster synthetic aperture radar (SAR) satellite data is essential for the timely damage investigation because the availability of data in a disaster area is usually limited. This article proposes a novel method to assess damage in urban areas by analyzing combined pre-disaster very high resolution (VHR) optical data and post-disaster polarimetric SAR (PolSAR) data, which has rarely been used in previous research because the two data have extremely different characteristics. To overcome these differences and effectively compare VHR optical data and PolSAR data, a technique to simulate polarization orientation angles (POAs) in built-up areas was developed using building orientations extracted from VHR optical data. The POA is an intrinsic parameter of PolSAR data and has a physical relationship with building orientation. A damage level indicator was also proposed, based on the consideration of diminished homogeneity of POA values by damaged buildings. The indicator is the difference between directional dispersions of the pre and post-disaster POA values. Damage assessment in urban areas was conducted by using the indicator calculated with the simulated pre-disaster POAs from VHR optical data and the derived post-disaster PolSAR POAs. The proposed method was validated on the case study of the 2011 tsunami in Japan using pre-disaster KOMPSAT-2 data and post-disaster ALOS/PALSAR-1 data. The experimental results demonstrated that the proposed method accurately simulated the POAs with a root mean square error (RMSE) value of 2.761 • and successfully measured the level of damage in built-up areas. The proposed method can facilitate efficient and fast damage assessment in built-up areas by comparing pre-disaster VHR optical data and post-disaster PolSAR data.superior to an optical sensor due to its relative insensitivity to the weather and illumination conditions. Several studies have investigated disaster-induced damage using pre and post-disaster SAR data [1][2][3][4][5][6]. Recently, damage investigation has been improved using fully polarimetric SAR (PolSAR) data because it allows more efficient results with its additional polarization scattering mechanism compared to a single polarization mode [5,6].However, pre-disaster SAR data is less abundant than pre-disaster optical data because optical sensors have traditionally been used. Therefore, the combination of pre-disaster optical data and post-disaster SAR data is regarded as desirable for rapid damage assessment [7]. Due to the significant radiometric and geometric differences between optical and SAR data, it is practically impossible to directly compare the two data. The previous studies using both data have been focused on well integrating different types of information derived from each data for land cover and land use mapping [8][9][10][11]. The research comparing the two data, particularly the pre-disaster optical data and the post-disaster SAR data, for damage mapping has been relatively less conducted [7,12].To overco...

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“…Buildings and structures are discerned in satellite or aerial images, either manually or automatically, by using an automatic building detection algorithm [29,30] followed by a RGA. Here, we note that [29] assumes that the building structures have convex rooftop sections.…”

Section: Discretization Of Spacementioning

confidence: 99%