期刊名称:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN:2194-9042
电子版ISSN:2194-9050
出版年度:2015
卷号:XL-7/W3
页码:587-592
DOI:10.5194/isprsarchives-XL-7-W3-587-2015
出版社:Copernicus Publications
摘要:While high-resolution remote sensing images have increased application possibilities for urban studies, the large number of shadow areas has created challenges to processing and extracting information from these images. Furthermore, shadows can reduce or omit information from the surface as well as degrading the visual quality of images. The pixels of shadows tend to have lower radiance response within the spectrum and are often confused with low reflectance targets. In this work, a shadow detection method was proposed using a morphological operator for dark pattern identification combined with spectral indices. The aims are to avoid misclassification in shadow identification through properties provided by them on color models and, therefore, to improve shadow detection accuracy. Experimental results were tested applying the panchromatic and multispectral band of WorldView-2 image from São Paulo city in Brazil, which is a complex urban environment composed by high objects like tall buildings causing large shadow areas. Black top-hat with area injunction was applied in PAN image and shadow identification performance has improved with index as Normalized Difference Vegetation Index (NDVI) and Normalized Saturation-Value Difference Index (NSDVI) ratio from HSV color space obtained from pansharpened multispectral WV-2 image. An increase in distinction between shadows and others objects was observed, which was tested for the completeness, correctness and quality measures computed, using a created manual shadow mask as reference. Therefore, this method can contribute to overcoming difficulties faced by other techniques that need shadow detection as a first necessary preprocessing step, like object recognition, image matching, 3D reconstruction, etc.
关键词:Shadow detection; high resolution; remote sensing; urban areas