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Shadow detection and building-height estimation using IKONOS data
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AI
DOI：10.1080/01431161.2010.517226.png)
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摘要
En 中文
The spectral confusion between shadow and water (or other dark surfaces) often results in suboptimal urban classification performances, especially from high-resolution satellite imagery (e. g. IKONOS). A classification method was developed to incorporate spatial indices of image objects to improve the shadow/water detection. A number of spatial indices, such as size, shape and spatial neighbour of image objects, were characterized to differentiate water and shadow objects. This generated superior shadow/water detection performance compared to a traditional per-field Extraction and Classification of Homogeneous Objects (ECHO) classification method. The user's accuracies for shadow and water classes were increased to 88% and 92%, compared to 80% and 76% obtained from the traditional ECHO classification approach. Furthermore, an automated approach was developed for shadow-length and corresponding building-height estimation. The accuracy assessment suggested good results for very high buildings, especially for isolated high-rise buildings.
Keyword:
IMAGE DATA
LAND-USE
EXTRACTION
COVER
CLASSIFICATION
MANAGEMENT
QUICKBIRD
OBJECTS
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期刊
IF:
2.6
论文数:
1.2W
被引数:
2.7W
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