Browsing by Subject "Automatic segmentations"
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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 urbanization using building patterns(2007) Doǧrusöz, E.; Aksoy, S.Automatic extraction of buildings and modeling of their spatial arrangements provide essential information for urban applications. This paper describes our work on modeling urbanization using spatial building patterns. Building detection is done using Bayesian classification of multi-spectral information. The individual buildings are used as textural primitives, and co-occurrence based spatial domain features and Fourier spectrum-based frequency domain features are used to model their repetitiveness and periodicity at particular orientations. These features are used to classify image neighborhoods as organized (regular) and unorganized (irregular). Experiments with high-resolution Ikonos imagery show that the proposed technique can be used for automatic segmentation of urban scenes and extraction of valuable information about urban growth.