Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge

Bouziani, Mourad; Goı̈ta, Kalifa; He, Dong-Chen

doi:10.1016/j.isprsjprs.2009.10.002

Cited by 195 publications

(120 citation statements)

References 18 publications

(24 reference statements)

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“…Post-classification comparison presumes that images are first rectified and classified, and then the classified images are compared to measure changes (Bouziani et al, 2010;Im and Jensen, 2005). The supervised (Ji et al, 2006) or unsupervised classification (Ghosh et al, 2011) can be of use.…”

Section: Related Workmentioning

confidence: 99%

Change Detection via Morphological Comparative Filters

Vizilter

Rubis

Zheltov

et al. 2016

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

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ABSTRACT:In this paper we propose the new change detection technique based on morphological comparative filtering. This technique generalizes the morphological image analysis scheme proposed by Pytiev. A new class of comparative filters based on guided contrasting is developed. Comparative filtering based on diffusion morphology is implemented too. The change detection pipeline contains: comparative filtering on image pyramid, calculation of morphological difference map, binarization, extraction of change proposals and testing change proposals using local morphological correlation coefficient. Experimental results demonstrate the applicability of proposed approach.

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Section: Related Workmentioning

confidence: 99%

Change Detection via Morphological Comparative Filters

Vizilter

Rubis

Zheltov

et al. 2016

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

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“…For example, Rottensteiner [13], Hermosilla et al [14], Grigillo et al [15], Malpica et al [16], and Tian et al [17] developed automatic approaches that combined image and surface data for building change detection. Knudsen and Olsen [18], Bouziani et al [19], and Liu et al [20] used existing vector and spectral data as inputs for building change detection. For map database updating, Matikainen et al [21] first combined airborne LiDAR and digital aerial images to conduct building change detection with the old map.…”

Section: Introductionmentioning

confidence: 99%

Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

Pang

Wang³

et al. 2014

Remote Sensing

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Building change detection is useful for land management, disaster assessment, illegal building identification, urban growth monitoring, and geographic information database updating. This study proposes an automatic method that applies object-based analysis to multi-temporal point cloud data to detect building changes. The aim of this building change detection method is to identify areas that have changed and to obtain from-to information. In this method, the data are first preprocessed to generate two sets of digital surface models (DSMs), digital elevation models, and normalized DSMs from registered old and new point cloud data. Thereafter, on the basis of differential DSM, candidates for changed building objects are identified from the points in the smooth areas by using a connected component analysis technique. The random sample consensus fitting algorithm is then used to distinguish the true changed buildings from trees. The changed building objects are classified as "newly built", "taller", "demolished" or "lower" by using rule-based analysis. Finally, a test data set consisting of many buildings of different types in an 8.5 km 2 area is selected for the experiment. In the test data set, the method correctly detects 97.8% of buildings larger than 50 m 2 . The accuracy of the method is 91.2%. Furthermore, to decrease the workload of subsequent manual checking of the result, the confidence index for each changed object is computed on the basis of object features.

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“…The algorithm for building map updating usually consist of two main steps: building change detection and updating. Based on available datasets in two different epochs, change detection methods can be classified into image-image and image-map comparison (Bouziani et al, 2010). In image-image strategy, two images, acquired in two different epochs compared based on common change detection techniques.…”

Section: Introductionmentioning

confidence: 99%

“…This strategy is useful for feature extraction and change detection because it contains advantageous information about the relevant feature class. Image-map change detection strategies are usually classified into post-extraction and map-guided methods (Bouziani et al, 2010). Post-extraction method is similar to post-classification methods that mentioned in image-image strategy.…”

Section: Introductionmentioning

confidence: 99%

“…Bentabet et al (2003) utilized existing map data to update road network. Bouziani et al (2010) has used contextual information that is derived from map data and then has performed object-based classification using fuzzy rules to find changed buildings. Le Bris et al (2011) has tested both image-map and image-image change detection strategy.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Building Map Updating Using Worldview-2 Stereo Images and Existing Outdated Building Map

Gharibi

Arefi

Rastiveis

et al. 2014

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

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ABSTRACT:Updating of existing geo-database, particularly in developing countries, is one of the important and essential issues in geospatial information systems. Numerous change detection methods have been proposed to resolve this issue. In this study, a novel approach is proposed for automatic building change detection and updating from available outdated building map and products of satellite stereo images. The proposed method consists of four steps. In the first step, preliminary building outline for a candidate building is extracted from available old polygon and DSM based on an active contour model. Then, some change detection rules are considered to find out whether or not the building has been changed. If changes are detected, the following third and fourth steps based on a hierarchical approach, run to generate precise changed building outline. The proposed method is tested and evaluated using sample dataset from Tunis City and the obtained results prove the feasibility of this algorithm for automatic building map updating using high resolution stereo satellite images.

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Automatic change detection of buildings in urban environment from very high spatial resolution images using existing geodatabase and prior knowledge

Cited by 195 publications

References 18 publications

Change Detection via Morphological Comparative Filters

Change Detection via Morphological Comparative Filters

Object-Based Analysis of Airborne LiDAR Data for Building Change Detection

Automatic Building Map Updating Using Worldview-2 Stereo Images and Existing Outdated Building Map

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