Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis
Abstract
:1. Introduction
- The method extracts individual buildings with different orientations and different structures from a semantic knowledge level by employing the ontology model.
- The meaningful image objects are obtained by a segmentation method that considers characteristics of SAR images, the topology and geometry characteristics of objects have special advantages for characterizing the building primitives.
- It is able to accurately extract the individual buildings using a single SAR image without any ancillary data.
2. Modeling the Individual Buildings in SAR Images
2.1. Characteristic Analysis of Large Individual Building
2.2. Ontological Semantic Analysis
2.3. Some Restrictions
- (1)
- The resolution of the SAR data ranges from 0.5 to 2 m;
- (2)
- The types of the large individual buildings mainly include large factory buildings and public buildings;
- (3)
- Buildings are assumed to have flat roofs and gable roofs. The minimum size of building is about 250 pixels in the meter-resolution SAR images;
- (4)
- Considering the particular scattering model of individual buildings in VHR SAR images, we hold that the individual building is made up of building primitives (layover area, corner reflection, roof area, and shadow area).
3. Proposed Method for Individual Building Extraction
3.1. Ontology Model of Individual Buildings and Building Primitives
- (1)
- An individual building is made up of building primitives (layover area, corner reflection, roof area, and shadow area). The layover area and corner reflection need to identify in the bright area of SAR image. The shadow area belongs to dark area.
- (2)
- The building orientation is determined by the direction of the layover area. It can be divided into three main directions: parallel to SAR flight direction, vertical and inclined.
- (3)
- Associated with the SAR incidence angle, the roof and dark area are in the specific side of the bright area.
- (4)
- The individual building types can be divided into flat-roof and gable-roof building, mainly determined by the width of bright areas.
- (5)
- After obtaining the building primitives, the object topology information and imagery parameter information are used to aggregate the primitives into individual buildings.
- (6)
- Buildings are considered to be large if their planar area is greater than or equal to 250 pixels in the meter-resolution SAR images.
- Bright area: Direction ∩ High brightness ∩ Rectangularity ∩ Area size;
- Roof: Specific side of the bright area ∩ Texture ∩ Area size ∩ Shape;
- Dark area: Low brightness ∩ Shape ∩ Specific side of the roof ∩ Area size.
- Flat-roof building: Narrow layover area ∩ Roof ∩ Total area ∩ Shadow position (Auxiliary);
- Gable-roof building: Wide layover area ∩ Roof ∩ Total area ∩ Shadow position (Auxiliary).
3.2. Object-Based Analysis of High Resolution SAR Image
3.2.1. SAR Image Segmentation
3.2.2. Characteristics of Objects
3.3. Individual Building Extraction Based on Ontological Semantic Analysis
4. Experimental Results
4.1. Properties of Data Sets and Experimental Setting
4.2. Results and Evaluations
5. Discussion
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Aspect Angle | Scattering Model | Building Sample | Corresponding Optical Image |
---|---|---|---|
φ = 90° | | | |
φ = 90° | | | |
φ = 0° | | | |
φ = 0° | | | |
Category | Name | The Formula of Features | The Significance of Features |
---|---|---|---|
Label | Object label | -- | Used to identify objects |
Gray feature | Mean | The average gray value of objects | |
Standard deviation | Used to represent gray level distribution of objects | ||
Texture feature | Homogeneity | Degree of object uniformity | |
Entropy | The amount of information | ||
Dissimilarity | Reflect the image sharpness and texture groove depth level | ||
Energy | Reflect the image gray distribution and texture fineness | ||
Shape feature | Area | Total pixel number of an object | |
Wide | -- | Width of the object minimum bounding rectangle | |
Rectangle degree | Ratio of object area and the object minimum bounding rectangle area | ||
Solidity | Ratio of object area and the object minimum bounding polygon area | ||
Density | It describes the extent of the object. The larger the value, the closer the object is square | ||
Imagery parameter feature | Main direction | -- | The angle between SAR range direction and the major axis of object external ellipse |
Orientation relationship of objects | -- | Determine the specific position relationship between objects according SAR range direction | |
Topological feature | adjacent objects | -- | The labels of all adjacent objects with object A |
Experimental Dataset Number | Image Sizes (Pixels) | Degree of Building Orientation Diversity | Other Features of the Buildings |
---|---|---|---|
1 | 800 × 1050 | basically the same | relatively dense |
2 | 1100 × 800 | quite different | several clusters |
3 | 1200 × 1200 | quite different | different sizes |
4 | 1240 × 700 | different | complicated roof |
5 | 2040 × 1360 | different | mixed with small houses |
Experimental Dataset Number | Building Number | Extraction | False Alarms | Split | Merged | Extraction Rate (%) | False Alarm Rate (%) |
---|---|---|---|---|---|---|---|
1 | 46 | 39 | 4 | 2 | 3 | 84.8 | 8.7 |
2 | 37 | 35 | 1 | 0 | 6 | 94.6 | 2.7 |
3 | 38 | 33 | 7 | 2 | 6 | 86.8 | 18.4 |
4 | 33 | 28 | 1 | 1 | 2 | 87.9 | 3.0 |
5 | 57 | 53 | 12 | 3 | 8 | 92.9 | 21.1 |
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Gui, R.; Xu, X.; Dong, H.; Song, C.; Pu, F. Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis. Remote Sens. 2016, 8, 708. https://doi.org/10.3390/rs8090708
Gui R, Xu X, Dong H, Song C, Pu F. Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis. Remote Sensing. 2016; 8(9):708. https://doi.org/10.3390/rs8090708
Chicago/Turabian StyleGui, Rong, Xin Xu, Hao Dong, Chao Song, and Fangling Pu. 2016. "Individual Building Extraction from TerraSAR-X Images Based on Ontological Semantic Analysis" Remote Sensing 8, no. 9: 708. https://doi.org/10.3390/rs8090708