automatic building detection for satellite images using igv and dsm

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Poojya Doddappa Appa College Of Engineering

Department Of Electronics and Communication

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Automatic Building Detection From Satellite Images Using Internal Gray Variance(IGV)

and Digital Surface Model(DSM)

Presented By:-AMIT(3PD14LCS01)

Guide:-Dr.GEETA HANJI

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Outline of The Presentation Introduction

Literature survey Problem Statement Objective of the project Description of the proposed work Working platform List of images for various steps used for detection of buildings Contributions Results Conclusion and Scope for Future Work References

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Introduction Automatic building extraction is

considered recently as an active research in remote sensing.

Main attention is to identify the buildings and segment it from the background within the aerial image.

Standard method considered is assuming that object(building) has four edges.

Helps in determine the damages after natural disasters, wars, defense system, city planning.

Minimizes human role in producing large-scale maps but also has a dramatic impact on time and cost.

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Literature Survey Muller and Zaum proposed an algorithm to detect and classify

building from a simple aerial image using a region growing algorithm[1]

Song and Shan proposed an algorithm to extract buildings from high-resolution color imagery.They have focused on the outline of building boundary and segmentation of building roof polygons or faces.[2]

Brunn.A, Weinder separated buidings and vegetation areas using height data and geometric information on DSM data.[3]

Mayunga, proposed a semi-automatic building extraction algorithm from satellite images by selecting a point on the boundary of each building, and by making use of iterative function.[4]

D.Chaudhuri proposed a automatic building detection by using morphology and internal gray variance.[5]

Problem Statement The algorithm (existing method R[5]) fails to accurately

detect buildings if the rooftop of the building is partially bright and partially dark.

Low rise building are not detected.

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Objectives of The Project To detect low raising buildings in the scene (buildings with zero shadow).

To detect buildings with partially bright and partially dark rooftop.

To obtain more Detection Percentage (DP) and keep Branch Factor (BF) low.

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Proposed Approach

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Image Enhancement To improve the contrast between target(building) and non target (non building) objects for high level processing.

It reduces(or increase) the brightness of dark(or bright) building structures in original image.

‘Opening’ and ‘Closing’ are used in combination as morphological filters.

Structuring element should be small to not remove the details and keep the computational cost low.

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Original Image Enhanced Image

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IGV Feature Extraction In manmade structure, the internal region is more

homogeneous than the outer region, i.e. internal pixels is low and border pixels is high.

Standard edge operation fails to detect borders, so we consider new technique “IGV feature”.

Technique reduces(or increase) the brightness of dark (or bright) building structures and blurs non manmade regions.

After application of the enhancement technique, the manmade objects appear more prominent in the image than the non manmade objects.

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Seed Point Detection In the seed point detection technique, we have used both enhanced image and IGV feature value.

First, seed points are detected using a multiseed technique of enhanced image. Then, the final seed points of IGV feature values are detected by using the seed points of enhanced image.

We choose to select the seed points from the enhanced image instead of the IGV feature images.

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Digital Surface Model To determine the height of the buildings we consider normalized DSM.

Global threshold method is generally used for the small vegetation group.

Binary classification scheme is used for the large vegetation group.

Bayesian classification method is used in our work which is improvement over binary classification scheme.

Using the height information of normalized DSM, we conclude that the point is the member of building segment.

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Clustering After applying multiseed clustering technique directly to enhanced image, many clustered regions are obtained.

We use nearest neighbor clustering technique.

Grouping of boundary pixels of manmade structures into a single cluster distinct.

Detection of threshold value is easier in binarization process.

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Binarization To distinguish internal region and border region in the image thresholding based binarization technique is used.

Threshold on the Bimodality detection is used.

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Thinning and Component Filtering Operations

The boundaries are thicker after use of image binarization process. To determine the accurate position of the building it is important to use the thinning process. We use Sobel Operator for the thinning process.

Figure:- Simple binary shape by thinning process

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Shadow Detection Technique We use the shadow to confirm that the detected object is a

building.

The important property of shadow is its lower luminance.

Enhanced image is used for the detection of shadow in the image.

Modified threshold based technique proposed by Otsu’s is used.

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False Alarm Reduction Technique

This step is used reduce false alarm obtained from the output of the thinning and component filtering step.

Shadow with respect to building dependents on look angle of satellite and position of sun.

To determine that the object identified is building we consider following steps.

1. Remove isolated shadows.2. Identify at least one nonzero pixel of the edge map image as

building seed.3. If exists, the eight neighbor connected component of edge

map image for the seed is kept as it is.

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Segmentation The final building detection step consider an enhanced image. Otsu method is used for segmentation. The output of the false alarm reduction technique is used.

Steps1. Find Four points.2. Find corresponding rectangular region of enhanced image.3. Apply Otsu’s algorithm and determine threshold value.4. Segment the rectangular region of enhanced image.5. Repeat the above steps for all connected edge regions.

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Working PlatformMinimum Hardware Requirement:

Intel Pentium IV processor. 1 GB RAM. 20 GB HDD.

Minimum Software Requirement:

Operating system: Windows XP, SP-3, 7. MATLAB R2011a.

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List of images for various steps used for detection of

buildings

Original Image Enhanced Image

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Shadow overlade Image Clustered Image of the IGV feature

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Binary Image Thinned Image

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Edge image by canny operator Small component filtered image

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Building edge image Final detected building

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Contributions The main contribution in our work is the use of Digital Surface Model.

The role of DSM is to determine the normalized height of the object.

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Results Different shades rooftop buildings are detected.

Original Image Proposed Method Existing Method

Image 1:- Image of size 404 × 402.

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Low raising buildings are detected.

Original Image Proposed Method Existing Method

Image 2:- Image of size 401 × 400.

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Elevated buildings are detected.

Original Image Proposed Method Existing Method

Image 3:- Image of size 404 × 403.

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Original Image Proposed Method Existing Method

Kalaburagi City Image.(source: Google Maps)

Image 4:- Image of size 401 × 401.

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Original Image Proposed Method Existing Method

Image 5:- Image of size 404 × 402.

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Original Image Proposed Method Existing Method

Image 6:- Image of size 404 × 402.

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Performance Data Set – 1 (Images from different satellite images).

ImageProposed Method Existing Method R[1]

TP FP TN DP(%) BF(%) TP FP TN DP(%) BF(%)

Fig. 1 245 7 17 93.51 2.77 100 2 162 38.16 1.96

Fig. 2 63 2 9 87.5 3.07 41 2 31 56.94 4.65

Fig. 3 32 2 8 80 5.88 24 0 16 60 0

MeanDP = 87.00% BF = 3.90% (overall)

DP = 51.70% BF = 2.20%

Fig. 5Fig. 6

Fig. 7

020406080

100 DP by Proposed Method

BF by Proposed Method

DP by Existing Method R[1]

BF by Existing Method R[1]

Perc

enta

ge (

%)

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Data Set – 2 (Kalaburagi City Images).

ImageProposed Method Existing Method R[1]

TP FP TN DP(%) BF(%) TP FP TN DP(%) BF(%)

Fig. 4 44 3 3 93.61 6.38 19 1 28 40.42 5

Fig. 5 32 7 0 100 17.94 27 0 5 84.37 0

Fig. 6 252 6 0 100 2.32 123 2 129 48.80 1.60

MeanDP = 97.87% BF = 8.88% (overall)

DP = 57.86% BF = 2.2%

Fig. 8Fig. 9

Fig. 10

020406080

100DP by Proposed Method

BF by Proposed Method

DP by Existing Method R[1]

BF by Existing Method R[1]

Perc

enta

ge (

%)

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Conclusion and Scope for Future Work

This work attempted to present a fully automated technique to precisely identify the building.

The results obtained proved that the proposed approach is very precise and effective in comparison with other approaches reported in the literature (specially the existing method R[5] ).

This work can be extended further to obtain the Detection Percentage as maximum as possible but keeping Branch Factor much less.

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Publication Details Amit Raikar and Geeta Hanji, “Automatic Building Detection from Satellite Images using Internal Gray Variance and Digital Surface Model,” International Journal of Computer Applications 145(3):25-33, July 2016.

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References1. S. Muller and D. W. Zaum, “Robust building detection in aerial images,” in Proc. Joint Workshop

ISPRS Ger. Assoc. Pattern Recog. Object Extr. 3-D City Models Road Databases Traffic Monit. Concepts Algorithms Eval., Vienna, Austria, 2005, vol. 36 (3/W 24),pp.143–148.

2. Y. Song and J. Shan, “Building extraction from high-resolution colour imagery based on edge flow driven active contour and JSEG,” in Proc. Int. Arch. Photogram. Remote Sens. Spatial Inf. Sci., Beijing, China, 2008, vol. XXXVII, pp. 185–190, Part B 3a.

3. Brunn, A., Weidner, U., 1997. Extracting buildings from digital surface models.International Archives of Photogrammetry, Remote Sensing, and Spatial Information Sciences 32 (Part34W2), 2734.

4. S. D. Mayunga, Y. Zhang, and D. J. Coleman, “Semi-automatic building extraction utilizing QuickBird imagery, ” IAPRS, vol. XXXVI, pp. 29–30, 2005, Part-3/W24.

5. D. Chaudhuri, Senior Member, IEEE, N. K. Kushwaha, A. Samal, Senior Member, IEEE, and R. C. Agarwal,” Automatic Building Detection From High-Resolution Satellite Images Based on Morphology and Internal Gray Variance” Manuscript received February 19, 2014; revised March 13, 2015; accepted April 09, 2015.

6. Shuang Zhou, Liang Mi, Hao Chen and Yishuang Geng “Building Detection in Digital Surface Model”

7. D. Chaudhuri, N. K. Kushwaha, and A. Samal, “Semi-automated road detection from high-resolution satellite images by directional morphological enhancement and segmentation techniques,” in Proc. IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 5, pp. 1538–1544, Oct. 2012.

8. D. Chaudhuri and A. Agrawal, “Split-and-merge procedure for image segmentation using bimodality detection approach,” Defence Sci.

J., vol. 60, no. 3, pp. 290–301, 2010.

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Thank You