Extracting Building Features from High Resolution Aerial Imagery for Natural Hazards Risk Assessment
نویسندگان
چکیده
Natural hazards risk assessment requires data on the built environment. This paper reports an image analysis method that can extract building features, mainly roof plan areas, for potential vulnerability analysis. Both pixeland object-based image processing methods are adopted. First, red/green/blue colour bands and image textures are incorporated in a supervised artificial neural network classifier to achieve good classification results of individual roofs. Second, within objectbased methods a hybrid of region and edge segmentations using colours and shapes is employed to extract useful spatial information of salient ground objects. Finally, the extracted spatial information is used to refine the pre-classified image of building roofs. An AUSIMAGE digital aerial image with a spatial resolution of 0.2 m is tested. The directly extracted data include roof locations, plan areas, and perimeters. Derived data can include distances from building centroids to street centre lines, and distances between adjacent buildings. Such extracted data can greatly assist detailed bushfire, hail, tornado, and flood risk assessment.
منابع مشابه
Integration of Deep Learning Algorithms and Bilateral Filters with the Purpose of Building Extraction from Mono Optical Aerial Imagery
The problem of extracting the building from mono optical aerial imagery with high spatial resolution is always considered as an important challenge to prepare the maps. The goal of the current research is to take advantage of the semantic segmentation of mono optical aerial imagery to extract the building which is realized based on the combination of deep convolutional neural networks (DCNN) an...
متن کاملComparison of Methods for Automated Building Extraction from High Resolution Image Data
This paper discusses a comparison analysis of different methods for automated building extraction from aerial and spaceborne imagery. Particularly approaches employing the Hough Transformation, Pattern Recognition Procedures and Texture Analysis are examined. Throughout this investigation advantages and disadvantages of the mentioned methods are examined, in order to see which procedures are su...
متن کاملAnalyzing fine-scale wetland composition using high resolution imagery and texture features
In order to monitor natural and anthropogenic disturbance effects to wetland ecosystems, it is necessary to employ both accurate and rapid mapping of wet graminoid/sedge communities. Thus, it is desirable to utilize automated classification algorithms so that the monitoring can be done regularly and in an efficient manner. This study developed a classification and accuracy assessment method for...
متن کاملObject-Based Classification of UltraCamD Imagery for Identification of Tree Species in the Mixed Planted Forest
This study is a contribution to assess the high resolution digital aerial imagery for semi-automatic analysis of tree species identification. To maximize the benefit of such data, the object-based classification was conducted in a mixed forest plantation. Two subsets of an UltraCam D image were geometrically corrected using aero-triangulation method. Some appropriate transformations were perfor...
متن کاملA Landcover Classification Scheme for High Resolution Imagery and It’s Application to Bushfire Protection in Residential Areas
With the increasing degree of global climate change, bushfires are becoming a major threat to human life and property. A risk assessment of bushfires is dependent on the availability of suitable information on the environment and human activities. Most of the spatial information for fire behaviour prediction is time-dependent, so it is both quite difficult and potentially very expensive to main...
متن کامل