A new building mask using the gradient of heights for automatic building extraction
- Siddiqui, Fasahat, Awrangjeb, Mohammad, Teng, Shyh, Lu, Guojun
- Authors: Siddiqui, Fasahat , Awrangjeb, Mohammad , Teng, Shyh , Lu, Guojun
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 International Conference on Digital Image Computing: Techniques and Applications (Dicta); Gold Coast, Australia; 30th November-2nd December 2016 p. 288-294
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- Description: A number of building detection methods have been proposed in the literature. However, they are not effective in detecting small buildings (typically, 50 m(2)) and buildings with transparent roof due to the way area thresholds and ground points are used. This paper proposes a new building mask to overcome these limitations and enables detection of buildings not only with transparent roof materials but also which are small in size. The proposed building detection method transforms the non-ground height information into an intensity image and then analyses the gradient information in the image. It uses a small area threshold of 1 m2 and, thereby, is able to detect small buildings such as garden sheds. The use of non-ground points allows analyses of the gradient on all types of roof materials and, thus, the method is also able to detect buildings with transparent roofs. Our experimental results show that the proposed method can successfully extract buildings even when their roofs are small and/or transparent, thereby, achieving relatively higher average completeness and quality.
- Authors: Siddiqui, Fasahat , Awrangjeb, Mohammad , Teng, Shyh , Lu, Guojun
- Date: 2016
- Type: Text , Conference proceedings
- Relation: 2016 International Conference on Digital Image Computing: Techniques and Applications (Dicta); Gold Coast, Australia; 30th November-2nd December 2016 p. 288-294
- Full Text:
- Reviewed:
- Description: A number of building detection methods have been proposed in the literature. However, they are not effective in detecting small buildings (typically, 50 m(2)) and buildings with transparent roof due to the way area thresholds and ground points are used. This paper proposes a new building mask to overcome these limitations and enables detection of buildings not only with transparent roof materials but also which are small in size. The proposed building detection method transforms the non-ground height information into an intensity image and then analyses the gradient information in the image. It uses a small area threshold of 1 m2 and, thereby, is able to detect small buildings such as garden sheds. The use of non-ground points allows analyses of the gradient on all types of roof materials and, thus, the method is also able to detect buildings with transparent roofs. Our experimental results show that the proposed method can successfully extract buildings even when their roofs are small and/or transparent, thereby, achieving relatively higher average completeness and quality.
An improved building detection in complex sites using the LIDAR height variation and point density
- Siddiqui, Fasahat, Teng, Shyh, Lu, Guojun, Awrangjeb, Mohammad
- Authors: Siddiqui, Fasahat , Teng, Shyh , Lu, Guojun , Awrangjeb, Mohammad
- Date: 2013
- Type: Text , Conference proceedings
- Relation: 2013 28th International Conference on Image and Vision Computing New Zealand, IVCNZ 2013; Wellington; New Zealand; 27th-29th November 2013; published in International Conference Image and Vision Computing New Zealand p. 471-476
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- Description: In this paper, the height variation in LIDAR (Light Detection And Ranging) point cloud data and point density are analyzed to remove the false building detection in highly vegetation and hilly sites. In general, the LIDAR points in a tree area have higher height variations than those in a building area. Moreover, the density of points having similar height values is lower in a tree area than in a building area. The proposed method uses such information as an improvement to a current state-of-the-art building detection method. The qualitative and object-based quantitative analyzes have been performed to verify the effectiveness of the proposed building detection method as compared with a current method. The analysis shows that proposed building detection method successfully reduces false building detection (i.e. trees in high complex sites of Australia and Germany), and the average correctness and quality have been improved by 6.36% and 6.16% respectively.
- Authors: Siddiqui, Fasahat , Teng, Shyh , Lu, Guojun , Awrangjeb, Mohammad
- Date: 2013
- Type: Text , Conference proceedings
- Relation: 2013 28th International Conference on Image and Vision Computing New Zealand, IVCNZ 2013; Wellington; New Zealand; 27th-29th November 2013; published in International Conference Image and Vision Computing New Zealand p. 471-476
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- Description: In this paper, the height variation in LIDAR (Light Detection And Ranging) point cloud data and point density are analyzed to remove the false building detection in highly vegetation and hilly sites. In general, the LIDAR points in a tree area have higher height variations than those in a building area. Moreover, the density of points having similar height values is lower in a tree area than in a building area. The proposed method uses such information as an improvement to a current state-of-the-art building detection method. The qualitative and object-based quantitative analyzes have been performed to verify the effectiveness of the proposed building detection method as compared with a current method. The analysis shows that proposed building detection method successfully reduces false building detection (i.e. trees in high complex sites of Australia and Germany), and the average correctness and quality have been improved by 6.36% and 6.16% respectively.
Building roof plane extraction from LIDAR data
- Awrangjeb, Mohammad, Lu, Guojun
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
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- Description: This paper presents a new segmentation technique to use LIDAR point cloud data for automatic extraction of building roof planes. The raw LIDAR points are first classified into two major groups: ground and non-ground points. The ground points are used to generate a 'building mask' in which the black areas represent the ground where there are no laser returns below a certain height. The non-ground points are segmented to extract the planar roof segments. First, the building mask is divided into small grid cells. The cells containing the black pixels are clustered such that each cluster represents an individual building or tree. Second, the non-ground points within a cluster are segmented based on their coplanarity and neighbourhood relations. Third, the planar segments are refined using a rule-based procedure that assigns the common points among the planar segments to the appropriate segments. Finally, another rule-based procedure is applied to remove tree planes which are generally small in size and randomly oriented. Experimental results on three Australian sites have shown that the proposed method offers high building detection and roof plane extraction rates.
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
- Full Text:
- Reviewed:
- Description: This paper presents a new segmentation technique to use LIDAR point cloud data for automatic extraction of building roof planes. The raw LIDAR points are first classified into two major groups: ground and non-ground points. The ground points are used to generate a 'building mask' in which the black areas represent the ground where there are no laser returns below a certain height. The non-ground points are segmented to extract the planar roof segments. First, the building mask is divided into small grid cells. The cells containing the black pixels are clustered such that each cluster represents an individual building or tree. Second, the non-ground points within a cluster are segmented based on their coplanarity and neighbourhood relations. Third, the planar segments are refined using a rule-based procedure that assigns the common points among the planar segments to the appropriate segments. Finally, another rule-based procedure is applied to remove tree planes which are generally small in size and randomly oriented. Experimental results on three Australian sites have shown that the proposed method offers high building detection and roof plane extraction rates.
A performance review of recent corner detectors
- Awrangjeb, Mohammad, Lu, Guojun
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2013
- Type: Text , Conference paper
- Relation: International Conference on Digital Image Computing: Techniques and Applications, 26 November 2013 to 28 November 2013 p. 157-164
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- Description: Contour-based corner detectors directly or indirectly estimate a significance measure (eg, curvature) on the points of a planar curve and select the curvature extrema points as corners. A number of promising contour-based corner detectors have recently been proposed. They mainly differ in how the curvature is estimated on each point of the given curve. As the curvature on a digital curve can only be approximated, it is important to estimate a curvature that remains stable against significant noises, for example, geometric transformations and compression, on the curve. Moreover, in many applications, for instance, in content-based image retrieval, a fast corner detector is a prerequisite. So, it is also a primary characteristic that how much time a corner detector takes for corner detection in a given image. In addition, different authors evaluated their detectors on different platforms using different evaluation systems. Evaluation systems that depend on human judgements and visual identification of corners are manual and too subjective. Application of a manual system on a large test database will be expensive. Therefore, it is important to evaluate the detectors on a common platform using an automatic evaluation system. This paper first reviews six most recent and highly performed corner detectors and analyse their theoretical running time. Then it uses an automatic evaluation system to analyse their performance. Both the robustness to noise and efficiency are estimated to rank the detectors.
- Authors: Awrangjeb, Mohammad , Lu, Guojun
- Date: 2013
- Type: Text , Conference paper
- Relation: International Conference on Digital Image Computing: Techniques and Applications, 26 November 2013 to 28 November 2013 p. 157-164
- Full Text:
- Reviewed:
- Description: Contour-based corner detectors directly or indirectly estimate a significance measure (eg, curvature) on the points of a planar curve and select the curvature extrema points as corners. A number of promising contour-based corner detectors have recently been proposed. They mainly differ in how the curvature is estimated on each point of the given curve. As the curvature on a digital curve can only be approximated, it is important to estimate a curvature that remains stable against significant noises, for example, geometric transformations and compression, on the curve. Moreover, in many applications, for instance, in content-based image retrieval, a fast corner detector is a prerequisite. So, it is also a primary characteristic that how much time a corner detector takes for corner detection in a given image. In addition, different authors evaluated their detectors on different platforms using different evaluation systems. Evaluation systems that depend on human judgements and visual identification of corners are manual and too subjective. Application of a manual system on a large test database will be expensive. Therefore, it is important to evaluate the detectors on a common platform using an automatic evaluation system. This paper first reviews six most recent and highly performed corner detectors and analyse their theoretical running time. Then it uses an automatic evaluation system to analyse their performance. Both the robustness to noise and efficiency are estimated to rank the detectors.
Integration of LIDAR data and orthoimage for automatic 3D building roof plane extraction
- Awrangjeb, Mohammad, Fraser, Clive, Lu, Guojun
- Authors: Awrangjeb, Mohammad , Fraser, Clive , Lu, Guojun
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 IEEE International Conference on Multimedia and Expo (ICME)
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- Description: Automatic 3D extraction of building roofs from remotely sensed data is important for many applications including city modeling. This paper proposes a new method for automatic 3D roof extraction through an effective integration of LIDAR (Light Detection And Ranging) data and multispectral orthoimagery. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups. The first group contains the ground points that are exploited to constitute a `ground mask'. The second group contains the non-ground points which are segmented using an innovative image line guided segmentation technique to extract the roof planes. The image lines extracted from the grey-scale version of the orthoimage are classified into several classes such as `ground', `tree', `roof edge' and `roof ridge' using the ground mask and colour and texture information from the orthoimagery. During roof plane extraction the lines from the later two classes are used to fit roof planes to the neighbouring non-ground LIDAR points. Finally, a new rule-based procedure is applied to remove planes constructed on trees. Experimental results show that the proposed method successfully removes vegetation and offers high extraction rates.
- Authors: Awrangjeb, Mohammad , Fraser, Clive , Lu, Guojun
- Date: 2013
- Type: Text , Conference paper
- Relation: 2013 IEEE International Conference on Multimedia and Expo (ICME)
- Full Text:
- Reviewed:
- Description: Automatic 3D extraction of building roofs from remotely sensed data is important for many applications including city modeling. This paper proposes a new method for automatic 3D roof extraction through an effective integration of LIDAR (Light Detection And Ranging) data and multispectral orthoimagery. Using the ground height from a DEM (Digital Elevation Model), the raw LIDAR points are separated into two groups. The first group contains the ground points that are exploited to constitute a `ground mask'. The second group contains the non-ground points which are segmented using an innovative image line guided segmentation technique to extract the roof planes. The image lines extracted from the grey-scale version of the orthoimage are classified into several classes such as `ground', `tree', `roof edge' and `roof ridge' using the ground mask and colour and texture information from the orthoimagery. During roof plane extraction the lines from the later two classes are used to fit roof planes to the neighbouring non-ground LIDAR points. Finally, a new rule-based procedure is applied to remove planes constructed on trees. Experimental results show that the proposed method successfully removes vegetation and offers high extraction rates.
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