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

Yıl 2016, Sayı: 108, 69 - 79, 01.12.2016

Ali Özgün Ok

https://doi.org/10.9733/jgg.090315.1t

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

Kaynakça

• Akçay H.G., Aksoy S., (2010), Building detection using directional spatial constraints. In: IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.1932-1935.
• Aksoy S., Yalniz I.Z., Tasdemir K., (2012), Automatic detection and segmentation of orchards using very high resolution imagery. IEEE Transactions on Geoscience and Remote Sensing, 50(8), 3117-3131.
• Aytekin Ö., Erener A., Ulusoy İ., Düzgün Ş., (2012), Unsupervised building detection in complex urban environments from multispectral satellite imagery, International Journal of Remote Sensing, 33(7), 2152-2177.
• Baltsavias E.P., (2004), Object extraction and revision by image analysis using existing geodata and knowledge:current status and steps towards operational systems. ISPRS Journal of Photogrammetry and Remote Sensing, 58(3-4), 129-151.
• Baumgartner A., Steger C., Mayer H., Eckstein W., (1997), Multiresolution, Semantic Objects, and Context for Road Extraction. In: Semantic Modeling for the Acquisition of Topographic Information From Images and Maps, Cambridge, Birkhauser-Verlag, pp.140-156.
• Benediktsson J.A., Pesaresi M., Arnason K., (2003), Classification and feature extraction for remote sensing images from urban areas based on morphological transformations. IEEE Transactions on Geoscience and Remote Sensing, 41(9), 1940- 1949.
• Boykov Y., Kolmogorov V., (2004), An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9), 1124-1137.
• Boykov Y., Veksler O., Zabih R., (2001), Fast approximate energy minimization via graph cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(11), 1222-1239.
• Brenner C., (2005), Building reconstruction from images and laser scanning. International Journal of Applied Earth Observation and Geoinformation, 6(3-4), 187-198.
• Das S., Mirnalinee T.T., Varghese K., (2011), Use of salient features for the design of a multistage framework to extract roads from high-resolution multispectral satellite images. IEEE Transactions on Geoscience and Remote Sensing, 49(10), 3906- 3931.
• Guo D., Weeks A., Klee H., (2007), Robust approach for suburban road segmentation in high-resolution aerial images. International Journal of Remote Sensing, 28(2), 307-318.
• Haala N., Kada M., (2010), An update on automatic 3D building reconstruction. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 570-580.
• Hinz S., Baumgartner A., (2000), Road Extraction in Urban Areas supported by Context Objects. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 33(B3), pp.405-412.
• Hinz S., Baumgartner A., Mayer H., Wiedemann C., Ebner H., (2001), Road Extraction Focussing on Urban Areas. In: Automatic Extraction of Man-Made Objects from Aerial and Space Images (III). Balkema Publishers, Rotterdam, pp.255-265.
• Inglada J., 2007. Automatic recognition of man-made objects in high resolution optical remote sensing images by SVM classification of geometric image features. ISPRS Journal of Photogrammetry and Remote Sensing, 62(3), 236-248.
• Izadi M., Saeedi P., (2012), Three-Dimensional polygonal building model estimation from single satellite images. IEEE Transactions on Geoscience and Remote Sensing, 50(6), 2254-2272.
• Karantzalos K., Paragios N., (2009), Recognition-driven two dimensional competing priors toward automatic and accurate building detection. IEEE Transactions on Geoscience and Remote Sensing, 47(1), 133-144.
• Katartzis A., Sahli H., (2008), A stochastic framework for the identification of building rooftops using a single remote sensing image. IEEE Transactions on Geoscience and Remote Sensing, 46(1), 259-271.
• Katartzis A., Sahli H., Pizurica V., Cornelis J., (2001), A model based approach to the automatic extraction of linear features from airborne images. IEEE Transactions on Geoscience and Remote Sensing, 39(9), 2073-2079.
• Lin C., Nevatia R., (1998), Building detection and description from a single intensity image. Computer Vision and Image Understanding, 72(2), 101-121.
• Liu J.G., (2000), Smoothing filter-based intensity modulation: A spectral preserve image fusion technique for improving spatial details. International Journal of Remote Sensing, 21(18), 3461- 3472.
• Mayer H., (1999), Automatic object extraction from aerial imagery-A survey focusing on buildings. Computer Vision and Image Understanding, 74(2), 138-149.
• Mayer H., Hinz S., Bacher U., Baltsavias E., (2006), A test of automatic road extraction approaches. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(3), pp.209-214.
• Mena J.B., (2003), State of the art on automatic road extraction for GIS update: a novel classification. Pattern Recognition Letters, 24(16), 3037-3058.
• Mena J.B., Malpica J.A., (2005), An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery. Pattern Recognition Letters, 26(9), 1201-1220.
• Ok A.O., Senaras C., Yuksel B., (2013), Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery. IEEE Transactions on Geoscience and Remote Sensing, 51(3), 1701-1717.
• Ok A.O., (2013), Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts. ISPRS Journal of Photogrammetry and Remote Sensing, 86(2013), 21-40.
• OTB, (2012), Orpheo Toolbox, http://www.orfeo-toolbox.org doxygen/, [Accessed 06 March 2012].
• Otsu N., (1975), A threshold selection method from gray-level histograms. Automatica, 11, 285-296.
• Poullis C., You S., (2010), Delineation and geometric modeling of road networks. ISPRS Journal of Photogrammetry and Remote Sensing, 65(2), 165-181.
• Rother C., Kolmogorov V., Blake A., (2004), Grabcut: interactive foreground extraction using iterated graph cuts, ACM Transactions on Graphics, 23(3), 309-314.
• Senaras C., Özay M., Vural F.Y., (2013), Building detection with decision fusion. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(3), 1295-1304.
• Shi W., Zhu C., (2002), The line segment match method for extracting road network from high-resolution satellite images. IEEE Transactions on Geoscience and Remote Sensing, 40(2), 511-514.
• Sirmacek B., Ünsalan C., (2011), A probabilistic framework to detect buildings in aerial and satellite images. IEEE Transactions on Geoscience and Remote Sensing, 49(1), 211-221.
• Teke M., Başeski E., Ok A.Ö., Yüksel B., Şenaras Ç., (2011), Multi- Spectral False Color Shadow Detection, In: Photogrammetric Image Analysis. Springer, Berlin, Heidelberg, pp.109-119.
• Tournaire O., Paparoditis N., (2009), A geometric stochastic approach based on marked point processes for road mark detection from high resolution aerial images. ISPRS Journal of Photogrammetry and Remote Sensing, 64(6), 621-631.
• Ünsalan C., Boyer K.L., (2005), A system to detect houses and residential street networks in multispectral satellite images. Computer Vision and Image Understanding, 98(3), 423-461.
• Ünsalan C., Sirmacek B., (2012), Road network detection using probabilistic and graph theoretical methods. IEEE Transactions on Geoscience and Remote Sensing, 50(11), 4441-4453.
• Wegner J.D., Montoya J., Schindler K., (2013), A higher-order CRF model for road network extraction. In:IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA.
• Wiedemann C., Hinz S., (1999), Automatic extraction and evaluation of road networks from satellite imagery. In: International Archives of Photogrammetry and Remote Sensing, 32(3-2W5), pp.95-100.