Automatic extraction and building change detection from digital surface model and multispectral orthophoto

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

doi:10.15292/geodetski-vestnik.2011.01.011-027

Cited by 19 publications

(22 citation statements)

References 3 publications

(3 reference statements)

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“…A detailed explanation of this phase is omitted from this article for issues of length, but a complete description can be found in a previous paper . A literature review revealed the concern that use of the NDVI for the elimination of vegetation from further data processing steps carries the risk that the shadowed parts of the buildings will also be eliminated from the processing [Grigillo et al, 2011]. For this reason, the same procedure was used to classify the shadows in the image.…”

Section: Ndvi Nir Vis Nir Vismentioning

confidence: 99%

Automatic three-dimensional features extraction: The case study of L'Aquila for collapse identification after April 06, 2009 earthquake

Baiocchi

Brigante

Dominici

et al. 2014

European Journal of Remote Sensing

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This paper illustrates an innovative methodology for post-earthquake collapsed building recognition, based on satellite-image classification methodologies and height variation information. Together, the techniques create a robust classification that seems to yield good results in this application field. In the first part of this study, two different feature extraction methodologies were compared, based respectively on pixel-based and object-oriented approaches. Then the classification results of the most accurate classification methodology, obtained on an eight band WorldView-2 monoscopic image, were completed with height variation information before and after the event. The height difference is calculated, comparing a photogrammetric DSM, obtained using a photogrammetric rigorous orbital model on some EROS-B 0.7 metre across-track stereopairs with a 'roof model' before the earthquake

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Section: Ndvi Nir Vis Nir Vismentioning

confidence: 99%

Automatic three-dimensional features extraction: The case study of L'Aquila for collapse identification after April 06, 2009 earthquake

Baiocchi

Brigante

Dominici

et al. 2014

European Journal of Remote Sensing

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show abstract

“…Lee et al, 2003, Cha et al, 2006, Koc San and Turker, 2010. The similar approach employing Radon transform is described in Grigillo et al (2011).…”

Section: References To Related Workmentioning

confidence: 99%

“…Additionally we used building outlines image. The elaboration of mask for the building area test and the building outlines image is in detail discussed in Grigillo et al (2011) within section 3.3. The mask for the building area test is made with a series of morphological operations from the initial building mask.…”

Section: Automatic Building Extractionmentioning

confidence: 99%

Urban Object Extraction From Digital Surface Model and Digital Aerial Images

Grigillo

Kanjir

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The paper describes two different methods for extraction of two types of urban objects from lidar digital surface model (DSM) and digital aerial images. Within the preprocessing digital terrain model (DTM) and orthoimages for three test areas were generated from aerial images using automatic photogrammetric methods. Automatic building extraction was done using DSM and multispectral orthoimages. First, initial building mask was created from the normalized digital surface model (nDSM), then vegetation was eliminated from the building mask using multispectral orthoimages. The final building mask was produced employing several morphological operations and buildings were vectorised using Hough transform. Automatic extraction of other green urban features (trees and natural ground) started from orthoimages using iterative object-based classification. This method required careful selection of segmentation parameters; in addition to basic spectral bands also information from nDSM was included. After the segmentation of images the segments were classified based on their attributes (spatial, spectral, geometrical, texture) using rule set classificator. First iteration focused on visible (i.e. unshaded) urban features, and second iteration on objects in deep shade. Results from both iterations were merged into appropriate classes. Evaluation of the final results (completeness, correctness and quality) was carried out on a per-area level and on a per-object level by ISPRS Commission III, WG III/4.

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“…Automatic building change detection from remote sensing data mainly falls into two categories (Grigillo et al, 2011). Firstly, in the direct approach, data acquired from one type of sensor at two different dates are directly compared to detect changes.…”

Section: Introductionmentioning

confidence: 99%

Building Change Detection From Lidar Point Cloud Data Based on Connected Component Analysis

Awrangjeb

Fraser

2015

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Building data are one of the important data types in a topographic database. Building change detection after a period of time is necessary for many applications, such as identification of informal settlements. Based on the detected changes, the database has to be updated to ensure its usefulness. This paper proposes an improved building detection technique, which is a prerequisite for many building change detection techniques. The improved technique examines the gap between neighbouring buildings in the building mask in order to avoid under segmentation errors. Then, a new building change detection technique from LIDAR point cloud data is proposed. Buildings which are totally new or demolished are directly added to the change detection output. However, for demolished or extended building parts, a connected component analysis algorithm is applied and for each connected component its area, width and height are estimated in order to ascertain if it can be considered as a demolished or new building part. Finally, a graphical user interface (GUI) has been developed to update detected changes to the existing building map. Experimental results show that the improved building detection technique can offer not only higher performance in terms of completeness and correctness, but also a lower number of undersegmentation errors as compared to its original counterpart. The proposed change detection technique produces no omission errors and thus it can be exploited for enhanced automated building information updating within a topographic database. Using the developed GUI, the user can quickly examine each suggested change and indicate his/her decision with a minimum number of mouse clicks.

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Automatic extraction and building change detection from digital surface model and multispectral orthophoto

Abstract: IZVLEČEK

Cited by 19 publications

References 3 publications

Automatic three-dimensional features extraction: The case study of L'Aquila for collapse identification after April 06, 2009 earthquake

Automatic three-dimensional features extraction: The case study of L'Aquila for collapse identification after April 06, 2009 earthquake

Urban Object Extraction From Digital Surface Model and Digital Aerial Images

Building Change Detection From Lidar Point Cloud Data Based on Connected Component Analysis

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