Browsing by Subject "Urban structures"
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Item Open Access Kentsel yapıların biçimbilimsel bölütlenmesi(IEEE, 2007-06) Akçay, H. Gökhan; Aksoy, SelimYüksek çözünürlükteki uzaktan algılamalı uydu görüntülerinde bölütleme kent uygulamalarında önemli bir problemdir çünkü elde edilen bölütlemelerle sınıflandırma için piksel tabanlı spektral bilginin yanında uzamsal ve yapısal bilgiler elde edilebilir. Bu bildiride, biçimbilimsel işlemlerle çıkarılan yapısal bilgi ve ana bileşenler analizi ile özetlenen spektral bilgi kullanılarak gürültüden etkilenmeyen bölütler elde eden bir yöntem sunduk. Yapılan deneyler yöntemin görüntü üzerinde komşuluk bilgisini ve spektral bilgiyi beraber kullanmayan başka bir yönteme göre daha düzgün ve anlamlı yapılar bulduğunu göstermiştir. Automatic segmentation of high-resolution remote sensing imagery is an important problem in urban applications because the resulting segmentations can provide valuable spatial and structural information that are complementary to pixel-based spectral information in classification. We present a method that combines structural information extracted by morphological processing with spectral information summarized using principal components analysis to produce precise segmentations that are also robust to noise. The experiments show that the method is able to detect structures in the image which are more precise and more meaningful than the structures detected by another approach that does not make strong use of neighborhood and spectral information.Item Open Access Modeling urban structures using graph-based spatial patterns(IEEE, 2007-07) Doǧrusöz, Emel; Aksoy, SelimWe introduce a new method for modeling the spatial arrangements of geospatial objects. As opposed to the existing approaches that are based on classifying images using pixel level methods, we propose to use objects as textural primitives and exploit their spatial patterns. First, the primitives are detected using spectral and morphological processing. Then, these primitives form the nodes of a graph where the neighborhood information is obtained through Voronoi tessellation of the image scene. Next, this graph is clustered by thresholding its minimum spanning tree. Finally, the resulting clusters are classified as regular or irregular by examining the distributions of the angles between neighboring nodes. Experiments using Ikonos images show that the application of the proposed model where buildings are used as the primitives and building groups are automatically classified as organized or unorganized can extract valuable information about urban development. © 2007 IEEE.Item Open Access Morphological segmentation of urban structures(IEEE, 2007-04) Akçay, H. Gökhan; Aksoy, SelimAutomatic segmentation of high-resolution remote sensing imagery is an important problem in urban applications because the resulting segmentations can provide valuable spatial and structural information that are complementary to pixel-based spectral information in classification. We present a method that combines structural information extracted by morphological processing with spectral information summarized using principal components analysis to produce precise segmentations that are also robust to noise. First, principal components are computed from hyper-spectral data to obtain representative bands. Then, candidate regions are extracted by applying connected components analysis to the pixels selected according to their morphological profiles computed using opening and closing by reconstruction with increasing structuring element sizes. Next, these regions are represented using a tree, and the most meaningful ones are selected by optimizing a measure that consists of two factors: spectral homogeneity, which is calculated in terms of variances of spectral features, and neighborhood connectivity, which is calculated using sizes of connected components. The experiments show that the method is able to detect structures in the image which are more precise and more meaningful than the structures detected by another approach that does not make strong use of neighborhood and spectral information. © 2007 IEEE.