Change detection using object features

Niemeyer, Irmgard; Marpu, Prashanth Reddy; Nussbaum, Sven

doi:10.1007/978-3-540-77058-9_10

Cited by 39 publications

(19 citation statements)

References 15 publications

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“…Based on different image segmentation strategies, there are three primary modes of multi-temporal image segmentation at present [40,41], namely single-temporal segmentation (STS), multi-temporal separate segmentation (MTSS), and multi-temporal combined segmentation (MTCS), as shown in Figure 3. Further details of the three primary modes of multi-temporal image segmentation are described in Niemeyer et al (2008) and Zhang et al (2017). In this paper, corresponding objects are extracted via vector-raster integration and coarse-to-fine segmentation under the boundary constraints of HVM.…”

Section: Mtis and Estimation Of Scale Parametersmentioning

confidence: 99%

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

Feng

Sui

et al. 2018

Remote Sensing

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Abstract:In the process of object-based change detection (OBCD), scale is a significant factor related to extraction and analyses of subsequent change data. To address this problem, this paper describes an object-based approach to urban area change detection (CD) using rotation forest (RoF) and coarse-to-fine uncertainty analyses of multi-temporal high-resolution remote sensing images. First, highly homogeneous objects with consistent spatial positions are identified through vector-raster integration and multi-scale fine segmentation. The multi-temporal images are stacked and segmented under the constraints of a historical land use vector map using a series of optimal segmentation scales, ranging from coarse to fine. Second, neighborhood correlation image analyses are performed to highlight pixels with high probabilities of being changed or unchanged, which can be used as a prerequisite for object-based analyses. Third, based on the coarse-to-fine segmentation and pixel-based pre-classification results, change possibilities are calculated for various objects. Furthermore, changed and unchanged objects identified at different scales are automatically selected to serve as training samples. The spectral and texture features of each object are extracted. Finally, uncertain objects are classified using the RoF classifier. Multi-scale classification results are combined using a majority voting rule to generate the final CD results. In experiments using two pairs of real high-resolution remote sensing datasets, our proposed approach outperformed existing methods in terms of CD accuracy, verifying its feasibility and effectiveness.

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Section: Mtis and Estimation Of Scale Parametersmentioning

confidence: 99%

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

Feng

Sui

et al. 2018

Remote Sensing

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“…Strategies range from the classification of the various objects in the images [14][15][16] (in this case, the change is immediate by comparison of the two classified data) to post-classifications or to classification of the change features [17][18][19]. As for the first situation, the quality of the result is strongly related to the segmentation.…”

Section: Updating Copernicus Products: Change Detection and Data Fusionmentioning

confidence: 99%

Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree

2016

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Abstract:Monitoring with high resolution land cover and especially of urban areas is a key task that is more and more required in a number of applications (urban planning, health monitoring, ecology, etc.). At the moment, some operational products, such as the "Copernicus High Resolution Imperviousness Layer", are available to assess this information, but the frequency of updates is still limited despite the fact that more and more very high resolution data are acquired. In particular, the recent launch of the Sentinel-2A satellite in June 2015 makes available data with a minimum spatial resolution of 10 m, 13 spectral bands, wide acquisition coverage and short time revisits, which opens a large scale of new applications. In this work, we propose to exploit the benefit of Sentinel-2 images to monitor urban areas and to update Copernicus Land services, in particular the High Resolution Layer imperviousness. The approach relies on independent image classification (using already available Landsat images and new Sentinel-2 images) that are fused using the Dempster-Shafer theory. Experiments are performed on two urban areas: a large European city, Prague, in the Czech Republic, and a mid-sized one, Rennes, in France. Results, validated with a Kappa index over 0.9, illustrate the great interest of Sentinel-2 in operational projects, such as Copernicus products, and since such an approach can be conducted on very large areas, such as the European or global scale. Though classification and data fusion are not new, our process is original in the way it optimally combines uncertainties issued from classifications to generate more confident and precise imperviousness maps. The choice of imperviousness comes from the fact that it is a typical application where research meets the needs of an operational production. Moreover, the methodology presented in this paper can be used in any other land cover classification task using regular acquisitions issued, for example, from Sentinel-2.

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“…The aim of this study was the forest thematic map-update which was tested on three 13x13 km 2 sub-areas in Canada. Other authors (Niemeyer et al, 2008) proposed an unsupervised CD and classification procedure based on object features extracted from QuickBird images. They applied multivariate alteration detection, accompanied by the maximum autocorrelation factor transformation to detect changes.…”

Section: Hybrid Change Detection Using Satellite Imagesmentioning

confidence: 99%

“…Studies using satellite images (Al-Khudhairy et al, 2005;McDermid et al, 2008;Niemeyer et al, 2008;Hofmann et al, 2008;Gamanya et al, 2009;Bouziani et al, 2010;Huang et al, 2014) did not use DSM for CD. CD was based solely on the analysis of image data.…”

Section: Current Problems and Proposed Solutionsmentioning

confidence: 99%

Nationwide Hybrid Change Detection of Buildings

Hron

Halounová

2016

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

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Commission VII, WG VII/5 KEY WORDS: Buildings, Change Detection, Pixel-based, Object-based, Aerial Photographs, Open-source ABSTRACT:The Fundamental Base of Geographic Data of the Czech Republic (hereinafter FBGD) is a national 2D geodatabase at a 1:10,000 scale with more than 100 geographic objects. This paper describes the design of the permanent updating mechanism of buildings in FBGD. The proposed procedure belongs to the category of hybrid change detection (HCD) techniques which combine pixel-based and object-based evaluation. The main sources of information for HCD are cadastral information and bi-temporal vertical digital aerial photographs. These photographs have great information potential because they contain multispectral, position and also elevation information. Elevation information represents a digital surface model (DSM) which can be obtained using the image matching technique. Pixel-based evaluation of bi-temporal DSMs enables fast localization of places with potential building changes. These coarse results are subsequently classified through the object-based image analysis (OBIA) using spectral, textural and contextual features and GIS tools. The advantage of the two-stage evaluation is the pre-selection of locations where image segmentation (a computationally demanding part of OBIA) is performed. It is not necessary to apply image segmentation to the entire scene, but only to the surroundings of detected changes, which contributes to significantly faster processing and lower hardware requirements. The created technology is based on open-source software solutions that allow easy portability on multiple computers and parallelization of processing. This leads to significant savings of financial resources which can be expended on the further development of FBGD.

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Change detection using object features

Cited by 39 publications

References 15 publications

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

A Novel Change Detection Approach for Multi-Temporal High-Resolution Remote Sensing Images Based on Rotation Forest and Coarse-to-Fine Uncertainty Analyses

Monitoring Urban Areas with Sentinel-2A Data: Application to the Update of the Copernicus High Resolution Layer Imperviousness Degree

Nationwide Hybrid Change Detection of Buildings

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