Abstract
Abstract. In this paper, the performance of different texture measures for detection of urban areas from SAR data is evaluated. The used texture measures are categorized into two groups, the first group include the SAR specific textures and the second one considers the general texture measures. ffmax is selected from the first category and LISA, SRPD, Wavelet measures and fractal dimensions are used as general texture measures. For a better discrimination, all texture measures are calculated and a PCA rotation is applied to them and the first PC is multiplied by the urban inhomogeneity parameter and the obtained image is segmented. The obtained results of this procedure comparing with the K-Means clustering algorithm show the better performance of this algorithm for urban area detection.
Highlights
In this paper, the performance of different texture measures for detection of urban areas from SAR data is evaluated
The SAR data is fused with both LIDAR and optic sensed data due to their vast coverage, frequent observation and data in the literature for this purpose
The Gomez-Chova et al, 2006; Soergel et al, 2005). usage of optical remote sensing data in VIS/NIR portion of the The purpose of this paper is to study the methods of second electromagnetic spectrum is usually limited by lots of factors category
Summary
The performance of different texture measures for detection of urban areas from SAR data is evaluated. One the best source SAR data with other kind of data for better classification of of information for urban area is remote sensing. Usage of optical remote sensing data in VIS/NIR portion of the The purpose of this paper is to study the methods of second electromagnetic spectrum is usually limited by lots of factors category. For these purpose defend texture measure are such as cloud contamination, atmospheric effect, and presence calculated using SAR data and their ability for detection of of difference materials in complex urban areas.
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