scholarly journals Building Change Detection from Historical Aerial Photographs Using Dense Image Matching and Object-Based Image Analysis

2014 ◽  
Vol 6 (9) ◽  
pp. 8310-8336 ◽  
Author(s):  
Stephan Nebiker ◽  
Natalie Lack ◽  
Marianne Deuber
Keyword(s):  
Image Analysis ◽  
Change Detection ◽  
Image Matching ◽  
Aerial Photographs ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Dense Image

scholarly journals NATIONWIDE HYBRID CHANGE DETECTION OF BUILDINGS

2016 ◽  
Vol XLI-B7 ◽  
pp. 497-504 ◽  
Author(s):  
V. Hron ◽  
L. Halounova
Keyword(s):  
Image Segmentation ◽  
Change Detection ◽  
Aerial Photographs ◽  
Surface Model ◽  
Sources Of Information ◽  
The Czech Republic ◽  
Object Based Image Analysis ◽  
Gis Tools ◽  
Object Based ◽  
Further Development

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.

scholarly journals Identifying Vegetation in Arid Regions Using Object-Based Image Analysis with RGB-Only Aerial Imagery

2019 ◽  
Vol 11 (19) ◽  
pp. 2308 ◽  
Author(s):  
Micha Silver ◽  
Arti Tiwari ◽  
Arnon Karnieli
Keyword(s):  
Image Analysis ◽  
Near Infrared ◽  
Vegetation Indices ◽  
Roc Curves ◽  
Threshold Value ◽  
Aerial Photographs ◽  
Object Based Image Analysis ◽  
Object Based ◽  
True Tree ◽  
Vegetation Patches

Vegetation state is usually assessed by calculating vegetation indices (VIs) derived from remote sensing systems where the near infrared (NIR) band is used to enhance the vegetation signal. However VIs are pixel-based and require both visible and NIR bands. Yet, most archived photographs were obtained with cameras that record only the three visible bands. Attempts to construct VIs with the visible bands alone have shown only limited success, especially in drylands. The current study identifies vegetation patches in the hyperarid Israeli desert using only the visible bands from aerial photographs by adapting an alternative geospatial object-based image analysis (GEOBIA) routine, together with recent improvements in preprocessing. The preprocessing step selects a balanced threshold value for image segmentation using unsupervised parameter optimization. Then the images undergo two processes: segmentation and classification. After tallying modeled vegetation patches that overlap true tree locations, both true positive and false positive rates are obtained from the classification and receiver operating characteristic (ROC) curves are plotted. The results show successful identification of vegetation patches in multiple zones from each study area, with area under the ROC curve values between 0.72 and 0.83.

scholarly journals RAPID MAPPING OF FORESTED LANDSLIDE FROM ULTRA-HIGH RESOLUTION UNMANNED AERIAL VEHICLE DATA

2018 ◽  
Vol XLII-3/W4 ◽  
pp. 171-176 ◽  
Author(s):  
R. Comert ◽  
U. Avdan ◽  
T. Gorum
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Heavy Rainfall ◽  
Landslide Inventory ◽  
Aerial Photographs ◽  
Surface Model ◽  
Object Based Image Analysis ◽  
Rapid Mapping ◽  
Object Based ◽  
Segmentation Image

<p><strong>Abstract.</strong> The Black Sea Region is one of the most landslide prone area due to the high slope gradients, heavy rainfall and highly weathered hillslope material conditions in Turkey. The landslide occurrences in this region are mainly controlled by the hydro-climatic conditions and anthropogenic activities. Rapid regional landslide inventory mapping after a major event is main difficulties encountered in this densely vegetated region. However, landslide inventories are first step and necessary for susceptibility assessment since considering the principle that the past is the key to the future thus, future landslides will be more likely to occur under similar conditions, which have led to past and present instability. In this respect, it is important to apply rapid mapping techniques to create regional landslide inventory maps of the area. This study presents the preliminary results of the semi-automated mapping of landslides from unmanned aerial vehicles (UAV) with object-based image analysis (OBIA) approach. Within the scope of the study, ultra-high resolution aerial photographs were taken with fixed wing UAV system on Aug 17, 2017 in the landslide zones which are triggered by the prolonged heavy rainfall event on August 12&amp;ndash;13, 2016 at Bartın Kurucaşile province. 10&amp;thinsp;cm resolution orthomosaic and Digital Surface Model (DSM) data of the area were produced by processing the obtained photographs. A test area was selected from the overall research area and semi-automatic landslide detection was performed by applying object-based image analysis. OBIA has been implemented in three steps: image segmentation, image object metric calculation and classification. The accuracy of the resulting maps is assessed by comparisons with expert based landslide inventory map of the area. As a result of the comparison, 80&amp;thinsp;% of the 240 landslides in the area were detected correctly.</p>

scholarly journals Detecção de mudanças baseada em objetos para o bairro do Camorim/Jacarepaguá

2016 ◽  
Vol 4 (3) ◽  
pp. 178
Author(s):  
Gabriel Lousada ◽  
Tainá Laeta ◽  
Maria Affonso Penna ◽  
Manoel Couto Fernandes
Keyword(s):  
Image Analysis ◽  
Change Detection ◽  
Rio De Janeiro ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Geographic Object

A detecção de mudanças baseada em objeto (Object-based Change Detection, OBCD) configura-se como uma área em grande crescimento dentro das pesquisas em Sensoriamento Remoto, isto porque, utilizando-se das possibilidades oferecidas pela análise de imagens baseada em objetos (Geographic Object-based Image Analysis, GEOBIA) é possível realizar a produção de mapeamentos de detecção de mudanças em uma única etapa, sem a necessidade da elaboração de mais de um mapa de cobertura da terra para posterior comparação entre as áreas que sofreram alterações. Partindo deste pressuposto, o presente trabalho buscou realizar um mapeamento de detecção de mudanças baseada em objetos para o bairro do Camorim em Jacarepaguá, zona oeste do município do Rio de Janeiro, entre os anos de 2011 e 2015. Os resultados obtidos indicam que 19,12% da área total do bairro sofreram modificações durante o intervalo de tempo em questão, tal resultado confirma que o bairro vem passando por grandes modificações para construção de infraestrutura dos Jogos Olímpicos de 2016. O processo de validação do mapeamento resultou em uma exatidão global de 0,80 e um índice Kappa de 0,60, considerado de boa qualidade para este tipo de mapeamento, especialmente se levado em conta sua replicabilidade para outras áreas.

scholarly journals NATIONWIDE HYBRID CHANGE DETECTION OF BUILDINGS

2016 ◽  
Vol XLI-B7 ◽  
pp. 497-504
Author(s):  
V. Hron ◽  
L. Halounova
Keyword(s):  
Image Segmentation ◽  
Change Detection ◽  
Aerial Photographs ◽  
Surface Model ◽  
Sources Of Information ◽  
The Czech Republic ◽  
Object Based Image Analysis ◽  
Gis Tools ◽  
Object Based ◽  
Further Development

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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