Class-Guided Building Extraction from Ikonos Imagery

Abstract

of stereo Ikonos imagery shows that 5-to 10-m contour lines can Recent high-resolution satellite images provide a valuable new be derived with the highest topographic standard (Toutin et al., data source for geospatial information acquisition. This paper 2001). As for geospatial feature extraction for topographic mapaddresses building extraction from Ikonos images in urban ping, Baltsavias et al. (2001a) and Fraser et al. (2001; 2002) presareas. The proposed approach uses the classification results ent results on building extraction from Ikonos stereo images. A of Ikonos multispectral images to provide approximate locacomparative study with the results obtained from aerial phototion and shape for candidate building objects. Their fine graphs concludes that about 15 percent of the building areas, as extraction is then carried out in the corresponding panchromeasured in aerial images, cannot be modeled using Ikonos matic image through segmentation and squaring. The ECHO images. An assessment based on 19 GPS surveyed check points classifier is used for supervised classification while the ISODATA at roof corners suggests that the Ikonos-derived building model algorithm is used for unsupervised classification and subsecan reach an accuracy of better than 1 meter both in planimetry quent image segmentation. The classification performance is and elevation (Baltsavias et al., 2001a). Sohn and Dowman evaluated using the classification confusion matrix, while the (2001) used a local Fourier transformation to analyze the domifinal building extraction results are assessed based on the nant orientation angle in a building cluster and extract rectilinmanually delineated results. A building squaring approach ear building outlines from Ikonos imagery based on a binarybased on the Hough transformation is developed that detects space partitioning tree. Dial et al. (2001) present an investigaand forms the rectilinear building boundaries. A number of tion on automated road extraction in wide suburban roads. sample results are presented to illustrate the approach and Regarding the fundamental methodology in geospatial feademonstrate its efficiency. It is shown that about 64.4 percent ture extraction from aerial and space images, abundant experiof the buildings can be detected, extracted, and accurately ence has been gained in the past few years. A collection of stateformed through this process. Remaining difficulties are high of-the-art articles can be found in the periodical proceedings percentage false alarm errors caused by the misclassification edited by Grü n et al. (1995), Grü n et al. (1997), and Baltsavias et of road and building classes as well as occlusion and shadows al. (2001b). Mayer (1999) presents a comprehensive survey on that may mislead the extraction process. the techniques used for image-based building extraction. These collections and surveys suggest a common understanding that Recent successfully launched satellites (Ikonos, EROS, Quick-multiple data sources and external knowledge or models about the buildings a...

Evaluation of Automatic Building Detection Approaches Combining High Resolution Images and LiDAR Data

“…Industrial buildings (Figure 4, right) are characterized for presenting large dimensions, and they are aimed to manufacture, transform, repair, store and distribute products. The evaluation at area level has been performed using a series of statistical parameters defined by McGlone and Shufelt [90] that have been repeatedly referred to in the literature [8,10,19,25,27,38,40,69,91,92]. Detected and reference buildings are spatially compared, and areas are categorized in four cases (see Figure 5 Using these cases, the following area level quality metrics are defined: The branching factor (Equation (1)) is a measure of the degree to which a system over-detects as buildings non-built areas.…”

“…Most building detection high-level vision techniques are based on image classification. Although some methodologies have been proposed to detect buildings using pixel-based classifications [17][18][19][20], in the majority of the studies the buildings are considered as objects, and automatic segmentation methods based on image homogeneity are used to create the image-objects. Regarding the classification process, image-objects are mainly characterized using descriptive features based on the spectral response, the image texture, or the shape of the objects [2,[21][22][23][24][25][26][27][28], or even using features derived from the wavelet transform [29,30].…”

Collective Sensing: Integrating Geospatial Technologies to Understand Urban Systems—An Overview

“…Remotely sensed derived variables, GIS thematic layers, and census data are three essential data sources for urban analyses, and their integration is thus a central theme in urban analysis. Since census data collected within spatial units can be stored as GIS attributes, the combination of census and remote sensing data combined with a GIS can be envisaged in three main ways [62] that relate to urban analyses: (i) remote sensing imagery have been used in extracting and updating transportation networks [63][64][65][66] and buildings [67][68][69][70], providing land use/cover data and biophysical attributes [17,58,59,[71][72][73], and detecting urban expansion [61,74,75]; (ii) Census data have been used to improve image classification in urban areas [60,76,77]; (iii) The integration of remote sensing and census data has been applied to estimate population and residential density [78][79][80][81][82][83][84][85][86][87][88], to assess socioeconomic conditions [89,90], and to evaluate the quality of life [91][92][93][94]. We note that census data are available at a number of different scales, as determined by independent (not remote sensing-based) spatial areas, typically down to census block levels.…”

Automatic Construction of Building Footprints From Airborne LIDAR Data

“…Manual derivation of building geometric data from a remote sensing image for a large area is cost prohibitive and time consuming. Therefore, numerous studies have been done to develop automated methods to extract footprints [3]- [6]. However, the success of the automated methods is limited due to the influence of sun shadow and relief displacement of high buildings in remote sensing images.…”