Second Generation Curvelet Transform for Building Extraction from High Resolution Satellite Imagery

Abstract

<jats:p> The process of automatic extraction of buildings from digital imagery has had major practical importance for many years in the area of data acquisition and updating of geographic information system (GIS) databases. This process also presents a huge scientific challenge for researchers as a result of the heterogeneous nature of the buildings, especially in the developing countries[Aytekin et al. 2009]. </jats:p><jats:p> Several techniques were used in building extraction for satellite images in this research paper, second generation curvelet transform will be introduced as an edge detection tool helping in the detection of buildings boundaries. Second generation curvelet transform provides optimally sparse representations of objects, which display smoothness except for discontinuity along the curve with bounded curvature [Candes et al. 2006]. Some papers have investigated this technique for edge detection in high resolution satellite imagery such as IKONOS or QuickBird, and microscopic imagery [Geback and Koumoutsakos 2009; Guha and Wu 2010; Zhenghai and Jianxiong 2009; Xiao et al. 2008], which show great potential of using curvelet transform in solving edge detection problems. However, until now, there is no research tackling the building detection problem using the curvelet transform in high resolution satellite imagery. </jats:p><jats:p> The algorithm consists of four main parts; first, data fusion between the panchromatic band (0.50 m resolution) and the multispectral ones (2.00 m resolution), to generate 8-spectral bands with a resolution of 0.50 m. Second, a Gaussian high pass filter is applied to enhance the edges. Third, using the curvelet transform, edges will be detected based on the fact that the values of curvelet coefficients are determined by how they are aligned in the real image. The more accurately a curvelet is aligned with a given curve in an image, the higher its coefficient value. Fourth, a filling process is performed for every closed boundary followed by calculation of statistics for these enclosed boundaries; such as area, major and minor axis, and compactness to extract the buildings. </jats:p>

Investigating the use of Sphaeroma serratum (Crustacea, Isopoda) as bio-indicator for heavy metals pollution in Lake Timsah, Suez Canal using alkaline comet assay technique

“…The lake has nearly a triangular shape with elongated sides extending roughly East-West. It has a surface area of about 8 km 2 and with an average depth of 11 meters and a volume of about 90 million cubic meters of water (El-Sharkawy, 2012). It is significantly polluted; the sources of pollution are essentially the raw sewage from the city network, industrial pollution from shore-line workshops, domestic sewage from unconnected areas adjoining the shore, agricultural drainage water, and possibly marine pollution (El-Moselhy and Yassien, 2005).…”

Enhanced Edge Detection Technique for High-Resolution Satellite Imagery Using a Curvelet-Canny Approach

“…Consequently, by arranging the coefficients of each level and taking the most significant part of them, this will enhance the edge information that represents the image part of interest. Then, the coefficients are reconstructed to get a new image called the edge map (Elsharkawy et al, 2011), as shown in Figure 11, where the edge parts are enhanced. Table 1 summarizes the total number of coefficient in each scale and the weight of each scale level used to reconstruct the edge map.…”

“…Direct thresholding of the edge map generated from the curvelet transforms was discussed in (Elsharkawy et al, 2011;Elsharkawy et al, 2012), for building extraction for high-resolution satellite imagery. In this research paper, an implementation of the second generation curvelet transform followed by the three main steps for the canny operator, gradient calculation, nonmaximal suppression and hysteresis, for edge detection of high-resolution satellite imagery.…”

New Combined Pixel/Object-Based Technique for Efficient Urban Classsification Using Worldview-2 Data

“…Based on shape and area characteristics, buildings will be extracted from the candidate parcels. The algorithm is summarized in figure 3; more details about this technique can be found in (Elsharkawy et al, 2011b) . The second step, of the algorithm deals with the classification step.…”

“…Taking advantage of all these properties a new pixel/object-based technique is introduced to extract shadows, water, vegetation, buildings, bare soil and asphalt roads for a complex scene in Ismailia city, 120 Kms to the north-east of Cairo the capital of Egypt. The new technique integrates the classification result from three new band ratios along with edge detection algorithm using the second generation curvelet transform (Elsharkawy et al, 2011b). The band ratio section showed very good results regarding water, shadow, asphalt roads, vegetation.…”