scholarly journals Automatic Building Map Updating Using Worldview-2 Stereo Images and Existing Outdated Building Map

2014 ◽  
Vol XL-2/W3 ◽  
pp. 131-136 ◽  
Author(s):  
M. Gharibi ◽  
H. Arefi ◽  
H. Rastiveis ◽  
H. Hashemi
Keyword(s):  
Change Detection ◽  
Active Contour ◽  
Active Contour Model ◽  
Detection Methods ◽  
Stereo Images ◽  
Geospatial Information ◽  
Novel Approach ◽  
Map Updating ◽  
Numerous Change ◽  
Geo Database

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 In this study, a novel approach is proposed for automatic building change detection and updating from available outdated building map and products of satellite stereo images.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.

scholarly journals Change Detection in Synthetic Aperture Radar Images Based on Fuzzy Active Contour Models and Genetic Algorithms

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Jiao Shi ◽  
Jiaji Wu ◽  
Anand Paul ◽  
Licheng Jiao ◽  
Maoguo Gong
Keyword(s):  
Synthetic Aperture Radar ◽  
Change Detection ◽  
Active Contour ◽  
Active Contour Model ◽  
Synthetic Aperture ◽  
Difference Image ◽  
Radar Images ◽  
Contour Model ◽  
The Difference ◽  
Aperture Radar

This paper presents an unsupervised change detection approach for synthetic aperture radar images based on a fuzzy active contour model and a genetic algorithm. The aim is to partition the difference image which is generated from multitemporal satellite images into changed and unchanged regions. Fuzzy technique is an appropriate approach to analyze the difference image where regions are not always statistically homogeneous. Since interval type-2 fuzzy sets are well-suited for modeling various uncertainties in comparison to traditional fuzzy sets, they are combined with active contour methodology for properly modeling uncertainties in the difference image. The interval type-2 fuzzy active contour model is designed to provide preliminary analysis of the difference image by generating intermediate change detection masks. Each intermediate change detection mask has a cost value. A genetic algorithm is employed to find the final change detection mask with the minimum cost value by evolving the realization of intermediate change detection masks. Experimental results on real synthetic aperture radar images demonstrate that change detection results obtained by the improved fuzzy active contour model exhibits less error than previous approaches.

A Novel Approach for Active Contour Initialization in 3D Medical Image Segmentation

2012 ◽  
Vol 239-240 ◽  
pp. 1004-1010
Author(s):  
Yan Jun Peng ◽  
Shuai Zhang
Keyword(s):  
Active Contour ◽  
Medical Image ◽  
Active Contour Model ◽  
Three Dimensional ◽  
Medical Image Segmentation ◽  
Initial Contour ◽  
Novel Approach ◽  
Fitting Method ◽  
Curve Fitting Method ◽  
Vector Field Convolution

Contour initialization is a big problem of the active contour model. Using the continuous features of the three-dimensional medical image, this paper proposes an initial contour prediction model. There are some changes in the boundary contours of the same object. We attribute these changes to continuous translation and similar deformation, and quantify into the centroid displacement and displacement of the point in the direction of Normal. The curve fitting method is used to predict the centroid displacement and the displacement of the points of the contours, which can provide more accurate prediction of changes in the contour. By predicting the initial contour, we have solved the contour initialization problem of the parametric active contour with external force using vector field convolution.

scholarly journals TOWARDS THE AUTOMATIC DETECTION OF GEOSPATIAL CHANGES BASED ON DIGITAL ELEVATION MODELS PRODUCED BY UAV IMAGERY

2019 ◽  
Vol XLII-2/W16 ◽  
pp. 47-53
Author(s):  
T. Bauman ◽  
O. Almog ◽  
S. Dalyot
Keyword(s):  
Change Detection ◽  
High Performance ◽  
Digital Elevation Models ◽  
Detection Methods ◽  
Geospatial Information ◽  
Point Change ◽  
Modeling Process ◽  
Digital Elevation ◽  
Invaluable Tool ◽  
Physical Changes

<p><strong>Abstract.</strong> Reliable and accurate geospatial-databases (Digital Elevation Models, DEMs) are an essential component of Geographic Information Systems (GIS). One of their most important uses is change detection – an invaluable tool for environmental interpretation and evidence-based action. High-performance and inexpensive Unmanned Aerial Vehicles (UAVs) are increasingly used for the acquisition of timely geospatial information (imagery) for the production of DEMs for geospatial change detection. DEMs produced from UAV imagery have very high resolution and very good internal accuracy. However, their absolute location accuracy is inferior to other mapping technologies. Therefore, existing change detection methods, which are based on the point-by-point comparison, will perform poorly when processing DEMs created from UAV imagery since they are limited in reliably separating real physical changes from artifacts related to DEM inherent inaccuracy or errors. This paper presents a novel methodology that overcomes these deficiencies, by implementing a hierarchical analysis and modeling process, in which a sequence of methods is used to automatically identify and match unique homological features, such as building corners or topographic maxima, in the various height models. These provide geospatial anchors that bring out local geospatial discrepancies between the models. Those are then used to "repair" (align) the models to the same geospatial reference system, at which point change-detection is performed. Experimental results showed that when calculating point-by-point height differences, 98.99% of the area was falsely classified as changed, whereas implementing our method adequately detected all the actual changes in the area with no false positives, correctly classifying 0.16% of the area as changed.</p>

scholarly journals Shoreline Extraction in SAR Image Based on Advanced Geometric Active Contour Model

2021 ◽  
Vol 13 (4) ◽  
pp. 642
Author(s):  
Xueyun Wei ◽  
Wei Zheng ◽  
Caiping Xi ◽  
Shang Shang
Keyword(s):  
Remote Sensing ◽  
Active Contour ◽  
Active Contour Model ◽  
Convergence Condition ◽  
Detection Methods ◽  
Visual Interpretation ◽  
Sar Images ◽  
Contour Model ◽  
Geometric Active Contour ◽  
Sea Area

Rapid and accurate extraction of shoreline is of great significance for the use and management of sea area. Remote sensing has a strong ability to obtain data and has obvious advantages in shoreline survey. Compared with visible-light remote sensing, synthetic aperture radar (SAR) has the characteristics of all-weather and all-day working. It has been well-applied in shoreline extraction. However, due to the influence of natural conditions there is a problem of weak boundary in extracting shoreline from SAR images. In addition, the complex micro topography near the shoreline makes it difficult for traditional visual interpretation and image edge detection methods based on edge information to obtain a continuous and complete shoreline in SAR images. In order to solve these problems, this paper proposes a method to detect the land–sea boundary based on a geometric active contour model. In this method, a new symbolic pressure function is used to improve the geometric active-contour model, and the global regional smooth information is used as the convergence condition of curve evolution. Then, the influence of different initial contours on the number and time of iterations is studied. The experimental results show that this method has the advantages of fewer iteration times, good stability and high accuracy.

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