Spatial Enhancement of Multispectral Images on Urban Areas

Aiazzi, Bruno; Baronti, Stefano; Capobianco, Luca; Garzelli, Andrea; Selva, Massimo

doi:10.1002/9780470979563.ch10

Cited by 1 publication

(1 citation statement)

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“…20 Pan-sharpening outputs an image of which spatial resolution is similar to a panchromatic image while carrying all the spectral bands present in the MS image. Different methods are available for spatial enhancement through pan-sharpening [e.g., intensity-huesaturation 21 and principal component analysis (PCA)].…”

Section: Satellite Images and Image Enhancement Techniquesmentioning

confidence: 99%

Hybrid method for building extraction in vegetation-rich urban areas from very high-resolution satellite imagery

Jayasekare

Wickramasuriya

Namazi-Rad

et al. 2017

J. Appl. Remote Sens

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A continuous update of building information is necessary in today's urban planning. Digital images acquired by remote sensing platforms at appropriate spatial and temporal resolutions provide an excellent data source to achieve this. In particular, high-resolution satellite images are often used to retrieve objects such as rooftops using feature extraction. However, high-resolution images acquired over built-up areas are associated with noises such as shadows that reduce the accuracy of feature extraction. Feature extraction heavily relies on the reflectance purity of objects, which is difficult to perfect in complex urban landscapes. An attempt was made to increase the reflectance purity of building rooftops affected by shadows. In addition to the multispectral (MS) image, derivatives thereof namely, normalized difference vegetation index and principle component (PC) images were incorporated in generating the probability image. This hybrid probability image generation ensured that the effect of shadows on rooftop extraction, particularly on light-colored roofs, is largely eliminated. The PC image was also used for image segmentation, which further increased the accuracy compared to segmentation performed on an MS image. Results show that the presented method can achieve higher rooftop extraction accuracy (70.4%) in vegetation-rich urban areas compared to traditional methods. Disciplines Engineering | Physical Sciences and Mathematics Publication DetailsJayasekare, A. S., Wickramasuriya, R., Namazi-Rad, M., Perez, P. & Singh, G. (2017). Hybrid method for building extraction in vegetation-rich urban areas from very high-resolution satellite imagery. Journal of Applied Remote Sensing, 11 (3), 036017-1-036017-12. Abstract. A continuous update of building information is necessary in today's urban planning. Digital images acquired by remote sensing platforms at appropriate spatial and temporal resolutions provide an excellent data source to achieve this. In particular, high-resolution satellite images are often used to retrieve objects such as rooftops using feature extraction. However, high-resolution images acquired over built-up areas are associated with noises such as shadows that reduce the accuracy of feature extraction. Feature extraction heavily relies on the reflectance purity of objects, which is difficult to perfect in complex urban landscapes. An attempt was made to increase the reflectance purity of building rooftops affected by shadows. In addition to the multispectral (MS) image, derivatives thereof namely, normalized difference vegetation index and principle component (PC) images were incorporated in generating the probability image. This hybrid probability image generation ensured that the effect of shadows on rooftop extraction, particularly on light-colored roofs, is largely eliminated. The PC image was also used for image segmentation, which further increased the accuracy compared to segmentation performed on an MS image. Results show that the presented method can achieve higher rooftop ex...

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Section: Satellite Images and Image Enhancement Techniquesmentioning

confidence: 99%