Automated Detection of Buildings and Roads in Urban Areas from VHR Satellite Images

Öz

Automated Detection of Buildings and Roads in Urban Areasfrom VHR Satellite ImagesIn this paper, we present an unsupervised approach to detect regions belonging to buildings and roads in urban areas from very high resolution VHR satellite images. The proposed approach consists of three mainstages. In the first stage, we extract information that is only related to building regions using shadow evidenceand probabilistic fuzzy landscapes. First, the shadow areas cast by building objects are detected, and the directional spatial relationship between buildings and their shadows is modeled with the knowledge of the illumination direction. Thereafter, each shadow region is handled separately and the initial building regions are identified by iterative graph-cuts designed in two-label partitioning. The second stage of the framework automatically classifies the image into four classes: building, shadow, vegetation, and others. In this step, the previously labeled building regions as well as the shadow and vegetation areas are involved in a fourlabel graph optimization performed on the entire image domain to achieve the unsupervised classification result. The final stage aims to extend this classification to five classes, including the road class. For that purpose, we extract the regions that might belong to road segments and utilize that information in a final graph optimization. This final stage eventually characterizes the regions belonging to buildings and roads. Experiments performed on twelve test images selected from GeoEye-1 VHR datasets show that the presented approach has the ability to extract the regions belonging to buildings and roads in a single graph theory framework

Anahtar Kelimeler

• Building Detection, Road Detection, Fuzzy Landscapes, Multi-label Graph Partitioning, VHR Satellite Imagery

Çok yüksek çözünürlüklü uydu görüntülerinden kentsel alanlardaki binaların ve yolların otomatik tespiti

Öz

Bu çalışmada çok yüksek çözünürlüklü uydu görüntülerinden kentsel alanlardaki bina ve yol alanlarının eğitimsiz olarak tespiti için bir yöntem sunulmuştur. Yöntem üç aşamadan oluşmaktadır. Birinci aşamada gölge ilgisi ve olasılık haritaları kullanılarak sadece bina bölgeleriyle ilgili bilgiler elde edilmiştir. İlk olarak, binanesnelerine ait gölge alanlar tespit edilmiş ve güneşin konum bilgisinden yararlanılarak binalar ve gölgeler arasında yönlü mekânsal ilişki modellenmiştir. Devamında her gölge alanı ayrı ayrı olarak ele alınmış ve ilk bina alanları iki-etiketli olarak gerçekleştirilen yinelemeli çizge-kesme yöntemiyle tanımlanmıştır. İkinci aşamanın amacı görüntüyü otomatik olarak dört sınıfa ayırmaktır: bina, gölge, bitki ve diğerleri. Bu aşamadadaha önceden bina, gölge ve bitki örtüsü olarak etiketlenmiş olan bölgeler ve herhangi bir etiket almamış olan diğer alanlar dört etiketli bir çizge-tabanlı optimizasyon işlemine tabi tutulmuştur. Son aşama ise bu sınıflamayı yol sınıfını da dâhil ederek beş sınıfa çıkarmayı hedeflemektedir. Bu amaçla, yol kısımlarına ait olması muhtemel bölgeler çıkarılmış ve bu bilgi optimizasyon işlemine dahil edilmiştir. Bu son aşama nihai olarak bina ve yol bölgelerini tanımlamaktadır. Çok yüksek çözünürlüklü GeoEye-1 veri setinden seçilen on iki adet test görüntüsü üzerinde yapılan değerlendirmeler, sunulan yaklaşımın bina ve yol alanlarını tek bir çizge-tabanlı yöntem altyapısı ile belirleyebilme yeteneğine sahip olduğunu göstermektedir

Anahtar Kelimeler

• Bina Tespiti, Yol Tespiti, Olasılık Haritaları, Çok Sınıflı Çizge-tabanlı Optimizasyon, Uydu Görüntüsü

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