CHAPTER 2 LITERATURE REVIEW
2.12 From desktop to web-based system
3.2.2 Extraction of Building Roofs
Extracting the whole building roof outline and roof planes is the first major phase of analysis involved in estimating the solar photovoltaic potential of a building. The rule- based classification process offers the possibility of automating the whole extraction process using fuzzy rule-sets. Using such rule-based classification system and incorporating the use of LiDAR-derived data with imagery, a hierarchical object extraction rule-set is designed in eCognition Developer 9 software to detect and extract the building roofs within the scope of this study. An overview of the processes is presented in Figure 3.3(a), while the outlines of the rule-set is given in Figures 3.3(b) and Figure 3.3(c).
Input LiDAR Data
Generation of Normalized LiDAR Point heights and interpolate
nDSM
Generation of Slope and Aspect Map
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Figure 3.3(a): Input, process and output workflow diagram for extraction of building roofs
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Figure 3.3(c): Workflow diagram for extraction of building roof planes
The detection and extraction of each building roof involves two stages, the first stage detects and extract the whole building roof outline, while the second stage decomposes each building roof into planes. The strategy adopted can be referred to as classification by elimination; objects of non-interest are classified as soon as detected, leaving the desired object as the last object to be classified. The aerial imagery and LiDAR-derived data such as nDSM, slope and aspect raster serve as input in eCognition Developer 9. The first process requires the aerial imagery bands to be segmented using the multiresolution segmentation algorithm (Segmentation 1) to create the initial image object primitives. A scale parameter of 85 was considered adequate for the segmentation, as this captures smaller building objects as a unit and roof planes of bigger building feature as a complete unit of image object. The weight
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of the red, green and blue bands are equally set to 1, for full participation in the segmentation process, while the shape and compactness parameters are set to 0.1 and 0.7 respectively. A lower shape and higher compactness parameter ensure image objects of similar colour are not fractured. With a successful segmentation, a merge region process is employed to merge all image objects with heights less 1.8 metres, from interrogating the LiDAR data, image objects with heights less than 1.8m are low- lying objects and vegetation. This cleans up the clutter of image objects leaving only image objects above the ground as object primitives.
With the low-lying image objects eliminated by merging them into a bigger image object, a customised object feature called “green index” is then designed to classify green image objects. The green index adopted in this is obtained by dividing the green band of the aerial imagery with the mean of all the three bands (G / (R + G + B)). This index performs better than the green-red index (G – R / G + R), which is adapted from the NDVI ratio, by replacing the NIR band with the green band. It is able to distinguish better between green objects and similar colour, such as blue-green roofs, when compared with the green-red index. Green objects were found to have an index of 0.36 and above. The closer the index value is to 1, the greener. Green image objects in this study are found to have an index of 0.36 and above. It is worth noting that the index value is based on the bit depth of the input imagery and therefore, not a universal or normalized index value. Using this index, a classification process (Assign class 1) is then added to classify green image objects, which are mostly trees and green building roofs. Using a similar approach another customised object feature is designed to classify (Assign class 2) shadowed areas. A shadow index is obtained by adding the mean of red, green and blue spectral information for each segmented image object (R+G+B). The shadow index identifies dark image objects as objects with index value less than 200, bright image objects have higher values of shadow index beyond 200. Like the green index value, the shadow index value range are also specific to the bit depth of the aerial imagery used in this study. Objects detected with this logic includes shadowed areas as well as very dark building roofs.
The previous classifications (Assign class 1 & 2) misclassify some potential building roofs, since only the spectral properties are utilised. However, tree objects are known to possess high slope value, as a result of varying discontinuity in its branches and sharp difference in elevation to the ground. A fuzzy logic of slope <= 450 is therefore
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employed in the subsequent classifications (Assign class 3 & 4) to declassify potential building roofs. Since building roof planes with slope greater 450 are not suited for solar
photovoltaic installations. Subsequently, the merged low-lying objects is classified (Assign class 5) as ground area.
At this stage, all elevated objects, save the building roof have been eliminated. It is then safe to classify (Assign class 6) the remaining unclassified image objects as building roof. The final stage of the whole roof extraction involves refining the building roof into a smoother or more regular building shape. The pixel-based object resizing (shrink and grow) and vector handling algorithms are utilised in this process, the process is further explained in Section 4.2.2. An export process is added to export the building roof polygons as a Shapefile. The building roof object now serves as an input for the second stage, which is the decomposition of the whole building roof outline into roof planes.
The chessboard segmentation algorithm (Segmentation 2) is used to break the whole roof into tiny units, setting the object size to 5 and domain to image object level. Using the aspect values, sets of fuzzy logics (Assign class 7 – 10) are then designed to classify each tiny building roof object into north, east, south or west. In determining what aspect values to use a basic quadrant was draw as shown in Figure 3.4.
Figure 3.4: Diagram showing cardinal directions and aspect values
The roof segments belonging to north are to be classified (Assign class 7) using “mean aspect >= 0o and <= 45o”; “mean aspect > 315o and <= 360o”. Any building roof
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are classified (Assign class 8) specifying “mean aspect > 45o and <= 135o” as condition
to be fulfilled. The south and the west segments are to be classified in a similar way. The next step involves aggregating (Aggregation 1 – 4) the classified segmented to form a roof plane. Algorithms to be employed includes “merge region” and “find enclosed by class”, the processes are further discussed in Section 4.2.2. This is then carried out on each of the cardinal directions. The last classification process (Assign class 11) detects and classifies roofs that are flat using the slope values. A threshold of mean slope <= 10o is adopted, as flat roof are not perfectly flat, but contains portions
gently sloped to drain the roof. Subsequent processes involve cleaning up the classification by smoothing and simplifying the roof plane edges. Finally, the roof planes are exported as Shapefiles and feature classes with attributes such as class name, slope, aspect and height. These attributes are to be utilised for calculating the solar photovoltaic potential of the building eventually.
The next sub-section presents the technique to be used to estimate the global solar radiation.