POPULATION DENSITY ESTIMATION USING TEXTONS

Yousra Javed*, Muhammad Murtaza Khan*, Jocelyn Chanussot**

*National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science (SEECS) Islamabad, Pakistan

** (GIPSA-Lab), Signal and Image Department, Grenoble Institute of Technology, Grenoble, France. yousra.javed,muhammad.murtaza@seecs.edu.pk, jocelyn.chanussot@gipsa-lab.grenoble-inp.fr

ABSTRACT

In this paper we propose an efficient method for population density estimation using textons and k nearest neighbor classifier (k-NN). Leung Malik (LM) filter bank is used for texture extraction (textons) from Google Earth Satellite Images and classification into high, medium, low population density and non-populated areas. We have tested the proposed method for 5 different images of cities of Pakistan at high resolution. Comparison of our results with those obtained using Grey Level Co-occurrence Matrix (GLCM) are also presented, indicating the effectiveness of the proposed method.

Index Terms— Textons, Classification, Population

Density

1. INTRODUCTION

In under developed countries satellite images of large regions are not readily available. However, with a utility like Google Earth access to such images is readily available. Generally, spectral information of multi-spectral and hyper-spectral images is used for classification purposes. The spectral information of processed images is not consistent and reliable. Hence, spectral techniques cannot be employed for classification purposes for non-true satellite images. In this context texture becomes an interesting information measure.

An image texture is a collection of “elements” or “patterns”, where the elements themselves may or may not have well-defined structures [6]. Texture can be analyzed with the help of structured and statistical approaches. According to the structured approach, an image texture is a set of primitive units in some regular or repeated pattern, i.e. artificially created textures. A statistical approach sees an image texture as a quantitative measure of the arrangement of intensities in a region, i.e. natural textures such as wood and rocks [6][7]. Texture in an image provides information about the spatial arrangement of intensities or colors in an image and is generally considered to be a function of the textures surface,

its albedo, illumination, view and orientation of camera. Changes in any of the above mentioned conditions may result in variations in the texture however this effect is reduced when the goal is not to classify the texture but a group of textures. In population density estimation each object is not classified separately and different textures can be combined together based upon size and this reduces the complexity of texture classification.

Recently, Liu et al. [3] have used texture information for population density estimation through regression analysis using texture features extracted from Grey Level Co-occurrence Matrix (GLCM), semi variance and spatial metrics. They have used regression coefficient R2 on Ikonos imagery, i.e. Near Infrared and Normalized Digital Vegetation Index (NDVI) images to find which texture feature gives the highest correlation of the estimated population with the data obtained from field survey. Their results highlight that highest correlation was achieved for NDVI images along with spatial metrics. In [4] the authors have used Google Earth images to find villages by using phase gradients of regions as texture features along with ADABOOST classifier.

We propose to use textons for image texture classification because they provide considerable speed efficiency and data reduction as compared to GLCM type approach while producing better estimates. This paper is organized as follows. Section 2 explains our texton based image classification methodology using the k-NN classifier. Section 3 details our experiments and the respective results on a dataset of five images along with their comparison with other methods. Section 4 concludes the paper.

2. METHODOLOGY

In this section, we describe how the texton based texture features can be used with a classifier for population density estimation. Textons refer to the fundamental structures in natural images and thus constitute the basic elements in visual perception [2][5]. They are the representative responses occurring after convolving an image with a set of

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filters, “filter banks”. A flow diagram of the proposed methodology is shown in Figure 2.

2.1. Texton Calculation To calculate textons for each of the four texture classes, their image blocks are passed through the LM filter bank [2]. At each pixel a response vector of size equal to the number of filters in the filter bank is formed. The resulting filter response vectors are divided into k clusters using k-means clustering algorithm. The response vectors corresponding to the k cluster centers are regarded as the textons of a particular texture class. In order to investigate how different number of training textons affect kNN’s classification results, we have experimented with different number of clusters i.e. 20, 40 and 80. Our experimental results have shown that best classification results are obtained when the number of clusters was set to k=80.

2.2. Texton Dictionary The texton dictionary is built by merging the textons of all four texture classes for a particular value of k. Therefore, merging 20, 40 and 80 textons per class result in a texton dictionary of 80,160 and 320 textons respectively.

Fig. 1. Procedure for building texton dictionary [1]

2.3. Texton Usage for Image Classification Image classification depends upon how the texture information (textons) is used. One approach is to build texton histograms for training and testing image blocks and use minimum norms to determine the texture class [1]. The other approach is to use a classifier which can be trained and tested based upon texton features. K nearest neighbor supervised classifier is a suitable candidate for this purpose since a block can be more accurately classified using a majority vote of its neighbors i.e. assigning it a class that is most common amongst its k nearest neighbor blocks. The two approaches are explained as follows:

2.3.1. K Nearest Neighbor based Classification A test image is first split up into blocks and textons for each block are calculated. Using the textons in the texton dictionary as training features for the classifier, the textons of an individual block form the sample to be classified by k-

NN. The block is assigned a texture class that has maximum of that block’s textons classified to it through k-NN. We use four nearest neighbor blocks, in our experiments.

3. EXPERIMENTS AND RESULTS

4. CONCLUSION

Fig. 2. Texton based classification using kNN

2.3.2. Texton histogram based ClassificationUsing the texton dictionary, a histogram telling how many pixels are assigned to each texton is calculated for each of the training images of a particular texture class. The histograms of the four classes are then used as models for classification [2]. During classification, the test image is split up into blocks and texton histogram for each block is calculated. The norm of each block’s histogram is computed with each of the four textures’ model histograms. The texton histogram giving the minimum norm determines the class of texture to which the image block belongs. This approach has been used in [1]. However, this approach is computationally intensive.

3. EXPERIMENTS AND RESULTS

In this section, we discuss our experimental setup and the results. Using execution time and kappa coefficient as measures, a comparison of various classification approaches is presented.

3.1. Dataset Our image dataset comprises of premium resolution (4800x2850 pixels) images from the major cities of Pakistan i.e. Islamabad, Lahore, Karachi, Peshawar and Faisalabad captured using Google Earth. The sample texture blocks of each class were extracted from these images with the help of experts who marked the ground truth using our marking tool. Our marking tool is a .Net application that allows an expert to mark/fit a colored polygon on a region in the Google Earth image and classify it among one of the four population density categories.

Build texton dictionary

Convolve image blocks

with filter

Construct filter response

vectors for each pixel

K-means clustering

of the responses

Classify a test image using kNN

Split test image in blocks

Calculate textons for each block

Pass textons in the dictionary as training examples to k-NN classifier and assign class to the block based on the classifications of its textons

Merge textons of four classes

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3.2. Class Labels Four different colors are used to represent each texture class as follows: • High population density(Red)

• Medium population density (Green)

• Low population density (Blue)

• Unpopulated i.e. Land/Vegetation (White) In the ground truth marked images, land/vegetation areas have been left as it is at some places. These pixels are counted as the fourth class i.e. white color. Sample ground truth marked images are shown in figure 3b and 4b.

3.3. Experimental Setup Since, the focus of this work is on exploring the speed up of texton based classification scheme; the experiments are performed on the block level. The block sizes used are 32x32 and 64x64 pixels. However, to compare the performance of the proposed algorithm with GLCM we have also performed pixel level classification for a single test image. We have used MATLAB for algorithm implementation and experiments.

In order to compare the accuracy of texton based classification using kNN with GLCM based supervised classification, we performed pixel wise classification on a test image that was used in [1]. Table 1 shows the quantitative comparison based on kappa coefficient, demonstrating that kNN based classification performs better.

3.4. Efficiency Comparison of Texton based Classification Approaches To calculate the time complexity of the proposed approach it was compared with the histogram based approach proposed in [1]. We run the algorithms on a batch of test images multiple times and then average the running time. It is to be noted that changing the value of k in k-means clustering for texton calculation does not affect the running time. On the other hand, smaller block sizes increase the number of loop iterations; thereby increasing the time complexity.

Table 2 displays the average time complexity values for the two texton based classification approaches. The results show that the kNN based approach is significantly faster than the histogram based approach owing to the elimination of texton histogram calculation module [1].

4. CONCLUSION

An efficient method for texton based classification is proposed which provides a twofold speed up. Textons of the four classes are used as features for training and testing the KNN classifier instead of building texton histograms. Experiments on 5 premium resolution Google Earth images show that this method is better than GLCM based approach

and achieves results analogous to previous texton based classification approach.

TABLE I

CLASSIFICATION COMPARISON BASED ON KAPPA COEFFICIENT

Image Texton based classification using kNN Classifier

GLCM based classification

Block size

64x64 Block size

32x32 Pixel wise

Pixel wise

City 0.42 0.38 0.40 0.20

Village 0.45 0.33 0.46 0.30

TABLE II

EFFICIENCY COMPARISON OF TEXTON BASED CLASSIFICATION APPROACHES

Texton based classification using KNN Classifier

Texton based classification using texton histograms

Block size 64x64

Block size 32x32

Block size 64x64

Block size 32x32

23.6 mins 25.86 mins 41.75 mins 44.11 mins

ACKNOWLEDGEMENTS The authors would like to thank the Higher Education Commission of Pakistan for providing funding for this research project under the IPFP program.

REFERENCES

[1] Y. Javed and M.M. Khan, “Image Texture Classification using Textons,” Proceedings of IEEE ICET 2011, Islamabad Pakistan, pp. 122-126, Sept. 2011

[2] M. Varma and A. Zisserman, “A statistical approach to texture classification from single images”, International Journal of Computer Vision, 62(1–2):61–81, Apr. 2005

[3] Liu, X.,Clarke,K., and Herold, M., “Population Density and Image Texture: a comparison study”, Photogrammetric Engineering & Remote Sensing, 72, 2 (2006), 187—196

[4] K. Murtaza, S. Khan and N. Rajpoot, “Villagefinder: Segmentation of nucleated villages in satellite imagery” British Machine Vision Conference, 2009

[5] S. C. Zhu, C. E. Guo, Y. Wu, and Y. Wang, “What are textons”, In Proc. Of European Conf. on Computer Vision (ECCV), 2002

[6]LS. Davis, “Foundations of Image Understanding”, Kluwer Academic Publishers Norwell, MA, USA, 2001 [7] Th. Leung and J. Malik, “Representing and Recognizing the Visual Appearance of Materials using Three-dimensional Textons”,In Proc. Of International Conf. on Computer Vision (ICCV), 1999

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a) Test image

b) Ground truth marked image

c) Texton based classification using kNN classifier (Block size 64x64)

d) Texton based classification using kNN classifier (Block size 32x32)

e) Texton based classification using k-NN classifier (Pixel wise)

f) GLCM based supervised classification

Fig. 3. Texton based classification using kNN on city area

a) Test image

b) Ground truth marked image

c) Texton based classification using kNN classifier (Block size 64x64)

d) Texton based classification using kNN classifier (Block size 32x32)

e) Texton based classification using k-NN classifier (Pixel wise)

f) GLCM based supervised classification

Fig. 4. Texton based classification using kNN on village area

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