Abstract
Quickly extraction of building information technology is an important application in urban development planning, electronic information, national defense and others. This paper takes Landsat-8 multispectral and panchromatic data as data source, using the local variance method to select the optimal segmentation scale, normalized difference vegetation index (NDVI) and the normalized building index (NDBI) and panchromatic brightness value of an object oriented classification rule extraction. The high vegetation coverage area of buildings, and through the spatial relationships and distinguishing feature of collections of buildings independent buildings and villages. The results showed that Google earth high resolution image analysis and accuracy evaluation. the results of the extraction based on the overall accuracy of village extraction was 83%, the accuracy of extraction of independent buildings was 70%, according to the L8 remote sensing data, object oriented classification method can quickly and accurately extract the high vegetation coverage area of the building.
Highlights
The built up areas is important components of national and regional geographical database
Extraction of building information technology is an important application in urban development planning, electronic information, national defense and others
The results showed that Google earth high resolution image analysis and accuracy evaluation. the results of the extraction based on the overall accuracy of village extraction was 83%, the accuracy of extraction of independent buildings was 70%, according to the L8 remote sensing data, object oriented classification method can quickly and accurately extract the high vegetation coverage area of the building
Summary
The built up areas is important components of national and regional geographical database. The extraction of urban built-up from remote sensing data is using traditional human interpretation way and pixel-based approach. The extracted objects by an object-based image analysis (OBIA) approach are more homogeneous than by pixels based approach and are closer to a visual human interpretation with high classification accuracy [3]. OBIA approach utilizing a rule based integration and directly builds into the existing knowledge pool, which can be applied on different satellite images. Recent developments in OBIA based on multi-resolution segmentation have revolutionized the processing of high resolution sensed data by offering effective computer-assisted classification techniques that come close to the quality of manual photo-interpretation, whilst being much faster, cheaper and more reproducible [4]. The spectral and geometry feature characteristic were selected to establish OBIA classification rules
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