**4. Results**

Several experiments have been completed to determine the best combination of PCA raster data for the building extraction process. A total of five experiments have been completed to determine the best scenario of building/roof extraction on a 1 sq. km area. The aerial photograph of these areas shows a total of 584 buildings; therefore, the accuracy of building extraction was measured using this number. A total of 20 buildings are chosen for the training sites, these buildings sites are used in all five approaches. **Table 1** is provided further below that gives a quantitative analysis of the process.

For the building segmentation process (**Figure 3**), a scale of 25, shape 0.5 and Compactness 0.5 was used, this parameter creates much smaller segments for smaller objects such as buildings. The objects (polygon) layer created by the segmentation is accompanied by an attribute table containing a unique identification field for every object. Segmentation is completed only on the first three bands of the PCA raster, the first three bands is the equivalent of the RBG in aerial photos. **Figure 4** illustrates the size of the polygons used in the segmentation process, the building segments shown are smaller, in some cases 7 segments represents a building, this allows for a better building extraction with a well-defined building outline.

The first test was conducted using the PCA of RGB and nDSM. This PCA raster has a total of four bands, red, green, blue and the height data from the nDSM. Segmentation was completed on the RGB bands only, however, feature extraction is completed on all bands. Image segmentation is recommended only on the RGB bands which provide, the color, shape, and textures of objects in the study area. After feature attraction, all segments are given attribute information from all the four bands, these include the mean values of RGB and height data form the nDSM. Using this approach, it is observed that most buildings were selected, however, there are many other features that are selected as buildings, these features are those that have similar height of buildings such as vegetation and fences. In addition,


#### **Table 1.**

*Result of area-based accuracy measures.*


**171**

**Figure 5.**

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos*

the outline of buildings is not well defined, although buildings are correctly classified, however, their shapes are not realistic of building outlines, this would require

of the datasets and therefore objects are not well defined (**Figure 6**).

The second test involves the PCA of RGB, NIR, nDSM and slope, this raster data contains six bands. Slope is considered and additional parameter that can aid in roof extraction, however the result of this approach is poor as many buildings are not classified and those that are selected, their outlines were not smooth and definitive. It is observed that additional bands in the PCA slightly lowers the spatial resolution

The third approach includes the PCA of RGB, nDSM and NDVI, a total of five bands. The NDVI (normalize difference vegetation index) is used in remote sensing to analyze the health of vegetation from green being healthy to red not healthy. NDVI was included to try to separate the objects that are green which are vegetation from other features that are not green such as buildings. The results (**Figure 7**) look promising where all buildings are selected, however, other features such as waterbodies and roads are classified as buildings, this observed to be because of the similar NDVI values of roads and waterbodies with the buildings. A closer observation shows that buildings that are close to each other are selected as one building

*DOI: http://dx.doi.org/10.5772/intechopen.92640*

several editing and adjustments (**Figure 5**).

*The size and scale of the segments used in the building extraction.*

**Figure 4.**

and most of their outline is not well defined.

*Building footprint extraction using PCA of RGB and nDSM.*

#### **Figure 3.**

*Segmentation parameters of the OBIA.*

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos DOI: http://dx.doi.org/10.5772/intechopen.92640*

#### **Figure 4.**

*Spatial Variability in Environmental Science - Patterns, Processes, and Analyses*

for smaller objects such as buildings. The objects (polygon) layer created by the segmentation is accompanied by an attribute table containing a unique identification field for every object. Segmentation is completed only on the first three bands of the PCA raster, the first three bands is the equivalent of the RBG in aerial photos. **Figure 4** illustrates the size of the polygons used in the segmentation process, the building segments shown are smaller, in some cases 7 segments represents a building, this allows for a better building extraction with a well-defined building outline. The first test was conducted using the PCA of RGB and nDSM. This PCA raster

has a total of four bands, red, green, blue and the height data from the nDSM. Segmentation was completed on the RGB bands only, however, feature extraction is completed on all bands. Image segmentation is recommended only on the RGB bands which provide, the color, shape, and textures of objects in the study area. After feature attraction, all segments are given attribute information from all the four bands, these include the mean values of RGB and height data form the nDSM. Using this approach, it is observed that most buildings were selected, however, there are many other features that are selected as buildings, these features are those that have similar height of buildings such as vegetation and fences. In addition,

> **Omission percentage**

RGB and nDSM 55.279% 4.229% 42.829% 95.770% 42.033%

40.336% 42.055% 59.663% 57.944% 41.634%

43.828% 2.417% 56.171% 97.582% 54.650%

37.608% 3.563% 62.391% 96.436% 60.985%

14.097% 7.270% 87.644% 93.220% 82.392%

**Completeness Correctness Quality**

**170**

**Figure 3.**

*Segmentation parameters of the OBIA.*

**PCA dataset Commission** 

RGB, NIR, nDSM and Slope

RGB, NIR, nDSM and NDVI

RGB, NIR and nDSM

*Result of area-based accuracy measures.*

NDVI

**Table 1.**

RGB, nDSM and

**percentage**

*The size and scale of the segments used in the building extraction.*

the outline of buildings is not well defined, although buildings are correctly classified, however, their shapes are not realistic of building outlines, this would require several editing and adjustments (**Figure 5**).

The second test involves the PCA of RGB, NIR, nDSM and slope, this raster data contains six bands. Slope is considered and additional parameter that can aid in roof extraction, however the result of this approach is poor as many buildings are not classified and those that are selected, their outlines were not smooth and definitive. It is observed that additional bands in the PCA slightly lowers the spatial resolution of the datasets and therefore objects are not well defined (**Figure 6**).

The third approach includes the PCA of RGB, nDSM and NDVI, a total of five bands. The NDVI (normalize difference vegetation index) is used in remote sensing to analyze the health of vegetation from green being healthy to red not healthy. NDVI was included to try to separate the objects that are green which are vegetation from other features that are not green such as buildings. The results (**Figure 7**) look promising where all buildings are selected, however, other features such as waterbodies and roads are classified as buildings, this observed to be because of the similar NDVI values of roads and waterbodies with the buildings. A closer observation shows that buildings that are close to each other are selected as one building and most of their outline is not well defined.

**Figure 5.** *Building footprint extraction using PCA of RGB and nDSM.*

**Figure 6.** *Building footprint extraction using PCA of RGB, NIR, slope and nDSM.*

#### **Figure 7.**

*Building footprint extraction using PCA of RGB, nDSM and NDVI.*

The fourth approach includes the PCA of RGB, NIR, nDSM and NDVI, a total of six bands. The NIR is introduced in the PCA to see if it can improve the extraction of buildings from other features. The result is an improvement from the third approach; however, many building outlines are still not well represented, which would require tedious editing and adjustments. Some editing tasks such as splitting polygons, and reshaping building boundaries will be exhaustive (**Figure 8**).

The fifth and final approach was conducted with the PCA of RGB, NIR and nDSM, a total of five bands. The results show a huge improvement from all other approaches in terms of selecting all features that are buildings as well as showing a well-defined boundary of building outline with a 92% extraction accuracy. Notice that there are very few other features that were classified as buildings using this approach (**Figure 9**).

**Table 2** shows a comparison of the five approaches completed. The number of all segments are the total segments of all features within the 1 sq.km of area for each approach. The segments classified as buildings are those segments that are assigned as buildings from the total of all segments. It is important to note that on average a total of six segments represents the entire outline of one building, this also depends

**173**

**Figure 8.**

**Figure 9.**

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos*

on the size of the building. Buildings correctly classified are those buildings that are correctly classified as buildings, but their shape and outline are not properly represented. Buildings properly represented are those buildings that are correctly classified, and their shape or outline is completely represented. The percentage of the properly represented buildings was calculated from the total number (584) of

From all the approaches discussed above the second approach which includes slope shows to be the worst result with 28% of accuracy of extraction. The slope band does not aid in the building extraction; however, the additional band has slightly lowered the resolution of the raster data. In OBIA, the color, shape, texture, compactness, and high resolution is needed for a smooth and realistic outline of buildings. The resolution of the aerial photos is important to maintain as this was used in the segmentation process. As shown in **Figure 10**, on the left is PCA of RGB, NIR and nDSM and the image to the right is PCA of RGB, NIR, nDSM and slope. The image on the right has reduced the image resolution, this can be seen around

actual building within the 1 sq. km area.

*Building footprint extraction using PCA of RGB, NIR and nDSM.*

the edges of buildings where it became fuzzy.

*DOI: http://dx.doi.org/10.5772/intechopen.92640*

*Building footprint extraction using PCA of RGB, NIR, nDSM and NDVI.*

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos DOI: http://dx.doi.org/10.5772/intechopen.92640*

**Figure 8.** *Building footprint extraction using PCA of RGB, NIR, nDSM and NDVI.*

**Figure 9.** *Building footprint extraction using PCA of RGB, NIR and nDSM.*

on the size of the building. Buildings correctly classified are those buildings that are correctly classified as buildings, but their shape and outline are not properly represented. Buildings properly represented are those buildings that are correctly classified, and their shape or outline is completely represented. The percentage of the properly represented buildings was calculated from the total number (584) of actual building within the 1 sq. km area.

From all the approaches discussed above the second approach which includes slope shows to be the worst result with 28% of accuracy of extraction. The slope band does not aid in the building extraction; however, the additional band has slightly lowered the resolution of the raster data. In OBIA, the color, shape, texture, compactness, and high resolution is needed for a smooth and realistic outline of buildings. The resolution of the aerial photos is important to maintain as this was used in the segmentation process. As shown in **Figure 10**, on the left is PCA of RGB, NIR and nDSM and the image to the right is PCA of RGB, NIR, nDSM and slope. The image on the right has reduced the image resolution, this can be seen around the edges of buildings where it became fuzzy.

*Spatial Variability in Environmental Science - Patterns, Processes, and Analyses*

The fourth approach includes the PCA of RGB, NIR, nDSM and NDVI, a total of six bands. The NIR is introduced in the PCA to see if it can improve the extraction of buildings from other features. The result is an improvement from the third approach; however, many building outlines are still not well represented, which would require tedious editing and adjustments. Some editing tasks such as splitting

**Table 2** shows a comparison of the five approaches completed. The number of all segments are the total segments of all features within the 1 sq.km of area for each approach. The segments classified as buildings are those segments that are assigned as buildings from the total of all segments. It is important to note that on average a total of six segments represents the entire outline of one building, this also depends

polygons, and reshaping building boundaries will be exhaustive (**Figure 8**). The fifth and final approach was conducted with the PCA of RGB, NIR and nDSM, a total of five bands. The results show a huge improvement from all other approaches in terms of selecting all features that are buildings as well as showing a well-defined boundary of building outline with a 92% extraction accuracy. Notice that there are very few other features that were classified as buildings using this

*Building footprint extraction using PCA of RGB, nDSM and NDVI.*

*Building footprint extraction using PCA of RGB, NIR, slope and nDSM.*

**172**

**Figure 7.**

**Figure 6.**

approach (**Figure 9**).


#### **Table 2.**

*Comparison of the five building footprint extraction approaches.*

Segmentation at a high resolution such as 0.1 m will allow a smoother and a more defined outline of the buildings. Image segmentation completed using 1-m spatial resolution such as the LiDAR nDSM and slope will show a jagged and irregular shape of buildings.

Using a visual binary comparison method for building extraction as shown in **Table 1**, The PCA of RGB, NIR and nDSM has shown to produce the best result of all five approaches. It shows buildings are correctly classified, properly represented, and has a total of 92% accuracy of extraction from the total number of buildings within the 1 sq. km area. Looking at the table above, it is noticeable that this approach has the least number of segments assigned to buildings with 7471. The smaller number of segments allow for better classification of buildings and present very few fragments of other features that are wrongly classified as buildings.

Another evaluation of the accuracy of the extraction process was conducted using the completeness and correctness method which is also known as Area-based accuracy measures. This method measures the completeness, correctness and quality of the building extraction process. The purpose of area-based accuracy measures is to obtain stable accuracy measurements. The area-based accuracy measures (i.e., correctness, completeness, and quality) are designed for OBIA evaluation [23].

**175**

**Figure 11.**

*Area-based accuracy measures, source: [24].*

process.

extraction.

buildings.

(**87.644%**).

(**93.220%**).

0.823 (**82.392%**).

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos*

In addition, this method can be used to calculate the commission and omission of building extraction. To complete this method, a reference building polygons and the extracted building polygons are needed. The reference data used is the 584 building

The completeness is the percentage of entities in the reference data that were detected, and the correctness indicates how well the detected entities match the reference data [25]. The quality of the results provides a compound performance metric that balances completeness and correctness [24]. TP (True Positive) are those areas correctly classified as buildings, FN (False Negative) are those areas that are classified as buildings but are not buildings based on the reference data. FP are those areas that are not classified as buildings during the extraction process, but are actual buildings based on the reference data. Error of commission is the same as FN, which are areas wrongly classified as buildings, and error of omission is the same as FP, which are areas that are buildings, but they are not extracted. Error of commission and omission are commonly used in the evaluation of building classification and are presented as percentages. An error of commission and omission, completeness, correctness and quality were completed for the five approaches of building extraction presented in **Table 2**. For illustration purposes the area-based accuracy measures was completed below for the PCA of RGB, NIR and nDSM using the equation in **Figure 11**. The total area for the reference data (584 buildings) is 72,360.357 sq. m. The total extracted area or classified buildings for the PCA of RGB, NIR and nDSM is 76,963.690 sq. m. The figures are illustrated below.

TP = 67454.350 sq. m. This is the correctly classified buildings in the extraction

FN = 9509.340 sq. m. This is the areas wrongly classified as buildings during

*Completeness = TP/(TP + FN)* = 67454.350/(67454.350 + 9509.340) = 0.876

*Correctness* = *TP/(TP + FP) =* 67454.350/(67454.350 + 4906.007) = 0.932

*Commission error = FN/TP* = 9509.340/67454.350 = 0.140 (**14.097%**). *Omission error = FP/TP* = 4906.007/67454.350 = 0.072 (**7.27%**).

The calculation illustrated above was completed for the other four building extraction approaches. The result is shown in **Table 1**. Using the area-based accuracy measures, the criteria for a complete and correct building extraction are low commission and omission percentage, and high completeness, correctness, and quality percentage rate. From all five approaches, the PCA of RGB, NIR and nDSM display this criterion with low commission and omission percentage and a high percentage of completeness (87.644%), Correctness (93.220%) and high quality of

FP = 4906.007 sq. m. This is the areas that are buildings but are not detected as

*Quality − TP/(TP + FN + FP) =* 67454.350/(67454.350 + 9509.340 + 4906.007) =

polygons within the 1 sq. km area. The equation used is shown in **Figure 11**.

*DOI: http://dx.doi.org/10.5772/intechopen.92640*

**Figure 10.** *Comparison of PCA images resolution.*

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos DOI: http://dx.doi.org/10.5772/intechopen.92640*

In addition, this method can be used to calculate the commission and omission of building extraction. To complete this method, a reference building polygons and the extracted building polygons are needed. The reference data used is the 584 building polygons within the 1 sq. km area. The equation used is shown in **Figure 11**.

The completeness is the percentage of entities in the reference data that were detected, and the correctness indicates how well the detected entities match the reference data [25]. The quality of the results provides a compound performance metric that balances completeness and correctness [24]. TP (True Positive) are those areas correctly classified as buildings, FN (False Negative) are those areas that are classified as buildings but are not buildings based on the reference data. FP are those areas that are not classified as buildings during the extraction process, but are actual buildings based on the reference data. Error of commission is the same as FN, which are areas wrongly classified as buildings, and error of omission is the same as FP, which are areas that are buildings, but they are not extracted. Error of commission and omission are commonly used in the evaluation of building classification and are presented as percentages. An error of commission and omission, completeness, correctness and quality were completed for the five approaches of building extraction presented in **Table 2**. For illustration purposes the area-based accuracy measures was completed below for the PCA of RGB, NIR and nDSM using the equation in **Figure 11**. The total area for the reference data (584 buildings) is 72,360.357 sq. m. The total extracted area or classified buildings for the PCA of RGB, NIR and nDSM is 76,963.690 sq. m. The figures are illustrated below.

TP = 67454.350 sq. m. This is the correctly classified buildings in the extraction process.

FN = 9509.340 sq. m. This is the areas wrongly classified as buildings during extraction.

FP = 4906.007 sq. m. This is the areas that are buildings but are not detected as buildings.

*Completeness = TP/(TP + FN)* = 67454.350/(67454.350 + 9509.340) = 0.876 (**87.644%**).

*Correctness* = *TP/(TP + FP) =* 67454.350/(67454.350 + 4906.007) = 0.932 (**93.220%**).

*Quality − TP/(TP + FN + FP) =* 67454.350/(67454.350 + 9509.340 + 4906.007) = 0.823 (**82.392%**).

*Commission error = FN/TP* = 9509.340/67454.350 = 0.140 (**14.097%**).

*Omission error = FP/TP* = 4906.007/67454.350 = 0.072 (**7.27%**).

The calculation illustrated above was completed for the other four building extraction approaches. The result is shown in **Table 1**. Using the area-based accuracy measures, the criteria for a complete and correct building extraction are low commission and omission percentage, and high completeness, correctness, and quality percentage rate. From all five approaches, the PCA of RGB, NIR and nDSM display this criterion with low commission and omission percentage and a high percentage of completeness (87.644%), Correctness (93.220%) and high quality of

**Figure 11.**

*Area-based accuracy measures, source: [24].*

*Spatial Variability in Environmental Science - Patterns, Processes, and Analyses*

**Segments classified as buildings**

**Buildings correctly classified**

122,200 29,676 578 247 42%

189,451 13,796 443 163 28%

164,788 22,946 584 326 56%

18,381 23,332 584 357 61%

122,651 7471 584 537 92%

**Buildings properly represented**

**Percentage with actual number of buildings (584)**

Segmentation at a high resolution such as 0.1 m will allow a smoother and a more defined outline of the buildings. Image segmentation completed using 1-m spatial resolution such as the LiDAR nDSM and slope will show a jagged and irregular

Using a visual binary comparison method for building extraction as shown in **Table 1**, The PCA of RGB, NIR and nDSM has shown to produce the best result of all five approaches. It shows buildings are correctly classified, properly represented, and has a total of 92% accuracy of extraction from the total number of buildings within the 1 sq. km area. Looking at the table above, it is noticeable that this approach has the least number of segments assigned to buildings with 7471. The smaller number of segments allow for better classification of buildings and present very few fragments of other features that are wrongly classified as buildings. Another evaluation of the accuracy of the extraction process was conducted using the completeness and correctness method which is also known as Area-based accuracy measures. This method measures the completeness, correctness and quality of the building extraction process. The purpose of area-based accuracy measures is to obtain stable accuracy measurements. The area-based accuracy measures (i.e., correctness, completeness, and quality) are designed for OBIA evaluation [23].

**174**

**Figure 10.**

*Comparison of PCA images resolution.*

shape of buildings.

**PCA dataset Number** 

RGB and nDSM

RGB, NIR, nDSM and Slope

RGB, nDSM and NDVI

RGB, NIR, nDSM and NDVI

RGB, NIR and nDSM

**Table 2.**

**of all segments**

*Comparison of the five building footprint extraction approaches.*

82.392%. In **Table 1**, it is noticeable that other approaches have higher correctness value, however their completeness and quality is poor.

There are no classification techniques that are 100% accurate, however, in OBIA, a rule-based classification can be used to improve the classification results. This was completed on the best approach discussed above involving the PCA of RGB, NIR and nDSM. The rule-based approach is only applicable where classification has been completed. It this case, it removes unwanted features that are not buildings by selecting features that have the characteristics of buildings such as size, shape, and height. Using the attribute information of classified buildings, a query is built to complete this step. The example below demonstrates this technique, where the image on the left shows features classified as buildings, looking closer at the image, the fences around these buildings are classified as buildings as well. Using the rule-based approach (**Figure 12**) this can be improved by selecting buildings within a certain height, as we know in most cases that fences are lower than houses. Therefore, a threshold is set between 6 m as the average height and 12 m as the average maximum height, this eliminates the fence as shown in the image on the right where it stays in red color, and the features that meet the criteria are selected shown in orange color.

The rule-based classification demonstrated a technique of improving the classification results by removing unwanted features based on their attribute information (**Figure 13**). However, geometrical information can be used as well, this is important where vegetations are classified as buildings. It is observed that the segments of vegetation are mostly circular in shape and the segments of buildings are rectangular. A threshold value of rectangularity can be used to eliminate vegetations that are wrong classified as buildings using their geometrical characteristics.

**Figure 12.** *Rule-based extraction of building footprints.*


**177**

**Figure 15.**

*Regularize building footprint outlines.*

**Figure 14.**

*Minimal rough edges of building outlines after extraction process.*

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos*

The result of the building extractions was converted to a feature class in ArcGIS where minimal post processing was performed. Zooming very close to a large scale of 1:250 of the building polygon you will notice that there are minor rough edges as shown in **Figure 14**. These minor rough edges cannot be seen at a scale of 1:1000. These rough or jagged edges were eliminated using the Regularize Building Footprint tool by setting a tolerance of 0.5 m and a precision of 0.25 m, this parameter was observed to produce the best results of cleaning the edges of buildings. The result is shown in **Figure 15** where a well-defined, smooth and realistic building

The building polygon was overlaid on the aerial photo and the results show a

Accurate building size and shape is important for damage assessment in flood modeling applications, as this will be used to determine the impact of a flood disaster on these structures. Ladyville village has a combination of medium and small buildings; however, it is observed that the most vulnerable populations are those that live in flood prone areas and those that live in tiny or small buildings. Proper representation of these small structures needs to be accurately represented

well-defined and accurate building or roof boundary (**Figure 16**).

*DOI: http://dx.doi.org/10.5772/intechopen.92640*

outline polygon is accomplished.

#### **Figure 13.**

*Building footprints extraction using attribute information.*

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos DOI: http://dx.doi.org/10.5772/intechopen.92640*

The result of the building extractions was converted to a feature class in ArcGIS where minimal post processing was performed. Zooming very close to a large scale of 1:250 of the building polygon you will notice that there are minor rough edges as shown in **Figure 14**. These minor rough edges cannot be seen at a scale of 1:1000.

These rough or jagged edges were eliminated using the Regularize Building Footprint tool by setting a tolerance of 0.5 m and a precision of 0.25 m, this parameter was observed to produce the best results of cleaning the edges of buildings. The result is shown in **Figure 15** where a well-defined, smooth and realistic building outline polygon is accomplished.

The building polygon was overlaid on the aerial photo and the results show a well-defined and accurate building or roof boundary (**Figure 16**).

Accurate building size and shape is important for damage assessment in flood modeling applications, as this will be used to determine the impact of a flood disaster on these structures. Ladyville village has a combination of medium and small buildings; however, it is observed that the most vulnerable populations are those that live in flood prone areas and those that live in tiny or small buildings. Proper representation of these small structures needs to be accurately represented

**Figure 14.**

**Figure 15.** *Regularize building footprint outlines.*

*Spatial Variability in Environmental Science - Patterns, Processes, and Analyses*

value, however their completeness and quality is poor.

82.392%. In **Table 1**, it is noticeable that other approaches have higher correctness

a rule-based classification can be used to improve the classification results. This was completed on the best approach discussed above involving the PCA of RGB, NIR and nDSM. The rule-based approach is only applicable where classification has been completed. It this case, it removes unwanted features that are not buildings by selecting features that have the characteristics of buildings such as size, shape, and height. Using the attribute information of classified buildings, a query is built to complete this step. The example below demonstrates this technique, where the image on the left shows features classified as buildings, looking closer at the image, the fences around these buildings are classified as buildings as well. Using the rule-based approach (**Figure 12**) this can be improved by selecting buildings within a certain height, as we know in most cases that fences are lower than houses. Therefore, a threshold is set between 6 m as the average height and 12 m as the average maximum height, this eliminates the fence as shown in the image on the right where it stays in red color, and the features that meet the criteria are selected shown

There are no classification techniques that are 100% accurate, however, in OBIA,

The rule-based classification demonstrated a technique of improving the classification results by removing unwanted features based on their attribute information (**Figure 13**). However, geometrical information can be used as well, this is important where vegetations are classified as buildings. It is observed that the segments of vegetation are mostly circular in shape and the segments of buildings are rectangular. A threshold value of rectangularity can be used to eliminate vegetations that are

wrong classified as buildings using their geometrical characteristics.

**176**

**Figure 13.**

**Figure 12.**

*Building footprints extraction using attribute information.*

*Rule-based extraction of building footprints.*

in orange color.

**Figure 16.** *Building footprint outlines overlaid on aerial photos.*

for proper analysis of the extent of the damages suffered. The images of tiny houses are provided in **Figure 17**, which gives an illustration of the size of some of the buildings in the study area. What is not shown are tiny buildings that are poorly constructed and in a very dilapidated condition, which may house sometimes a family of 4 or 5 people.

During field collection and verification, it was observed that some of these small buildings were not classified in the LiDAR data. This means that their roof outline cannot be extracted. However, with OBIA process using a combination of aerial photos and Lidar height information, these small structures were successfully extracted as well. The image on the left in **Figure 18** shows small building that were not classified, with red points representing buildings. The image on the right is the result of the OBIA building extraction, which clearly shows that it has extracted

**179**

process.

**Figure 18.**

**5. Discussion and conclusion**

important, such as in flood damage assessment.

from other objects.

**Acknowledgements**

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos*

the roof shape of these small buildings. Small buildings in the top left illustrate this

A semi-automated object-based building extraction with limited post processing using the PCA image fusion technique is presented. The results show a very promising technique for precise and high-resolution extraction of buildings in urban areas using LiDAR derived height information (nDSM) combined with aerial photos (RGB and NIR). These data complement each other by providing mutual benefits in the extraction process. The RGB provided high resolution image with color which is very important in the segmentation process of OBIA to group pixels into segments, the nDSM provide height information to separate elevated structures such as buildings from other features and the NIR provides information to separate vegetation

The extraction process was completed at a high spatial resolution of 0.1 m (10 cm). The high resolution PCA of aerial photos and LiDAR nDSM allows the building to maintain its well defined and smooth shape. The result of this study can be applied to various scenarios where accurate size and shape of buildings are

The author would like to acknowledge the Ministry of Works, Belize C.A for providing the LiDAR datasets and the aerial photographs for the study area.

This approach successfully extracts buildings from the study area, and as discussed above, minimal post processing was required. An average of 2 hours is required to complete this process. With faster computers, this time could be significantly reduced. Most of the time is spent on image preparation, PCA analysis and conversion between different raster types. This approach is a significant improvement where approximately 600 buildings can be properly represented within this period. The building's shape is well preserved. Even buildings that have a combined roof type as zinc and concrete were well outlined. The extraction process was completed at a high spatial resolution of 0.1 m (10 cm). The high resolution PCA of aerial photos and LiDAR nDSM allows the building to maintain its smooth outline with a completeness of 87.644%, Correctness of 93.220% and a quality of 82.392%.

*DOI: http://dx.doi.org/10.5772/intechopen.92640*

*Tiny building footprints extraction using PCA and OBIA method.*

**Figure 17.** *Example of tiny buildings not classified as buildings in LiDAR.*

*High-Resolution Object-Based Building Extraction Using PCA of LiDAR nDSM and Aerial Photos DOI: http://dx.doi.org/10.5772/intechopen.92640*

**Figure 18.** *Tiny building footprints extraction using PCA and OBIA method.*

the roof shape of these small buildings. Small buildings in the top left illustrate this process.

This approach successfully extracts buildings from the study area, and as discussed above, minimal post processing was required. An average of 2 hours is required to complete this process. With faster computers, this time could be significantly reduced. Most of the time is spent on image preparation, PCA analysis and conversion between different raster types. This approach is a significant improvement where approximately 600 buildings can be properly represented within this period. The building's shape is well preserved. Even buildings that have a combined roof type as zinc and concrete were well outlined. The extraction process was completed at a high spatial resolution of 0.1 m (10 cm). The high resolution PCA of aerial photos and LiDAR nDSM allows the building to maintain its smooth outline with a completeness of 87.644%, Correctness of 93.220% and a quality of 82.392%.
