OBJECT ORIENTED HIERARCHICAL CLASSIFICATION OF HIGH RESOLUTION REMOTE SENSING IMAGES

Ghariani Ons, Riadh Tebourbi

URISA, Higher school of communication of Tunis Sup’Com

ABSTRACT

The appearance of the satellite images in very high resolution is a real opportunity for the geographical identification of objects in urban zones. These images provide a huge amount of data about land cover surface and allow the perception of objects on the ground which was not observable in lower resolutions e.g. Ikonos images. Nevertheless, their heterogeneousness perturbs the methods of classic classification, also called pixel based methods. In this paper we propose an object oriented approach for extracting urban objects. Our approach is divided into two steps: the first is a hierarchical segmentation based on region-merging according spatial (texture) and spectral (NDVI, IB) criteria. The second is a regions classification using the non supervised approach.

Index Terms— VHR satellite image, remote sensing, hierarchical segmentation, watershed, NDVI, IB, Texture

1. INTRODUCTION

High resolution remote sensing imagery provides potential details for the extraction of the object features in urban environments. However, data extraction using traditional pixel-based classification approaches become unsatisfactory as it is shown on [1] [2]. In fact, urban morphology has complex structures characterized by spectral and spatial heterogeneity. Furthermore these images present low spectral resolution (bands blue, green, red and near infra-red) which makes it difficult to distinguish several urban targets presenting a similar behavior in the visible wavelengths. In order to overcome these drawbacks, other classification methods must be used which are not restricted to pixel values. An object-oriented approach allows the introduction of both spectral and spatial features in the image classification. In this paper, we present an object-oriented hierarchical classification approach using Ikonos multispectral data.

2. DESCRIPTION OF THE METHOD

In the object oriented classification method, not single pixels

are classified but homogenous image objects or regions are extracted during the segmentation step. The first step of the region-based process is to perform initial morphological watershed segmentation that consists in the grouping pixels on homogenous regions. The watershed method is detailed on [3] gives as an over- partition of the image. To reduce the number of initial segments, we apply an anisotropic diffusion [4] for its efficiency of smoothing the noisy images while preserving the sharp edges on image. Afterwards, the resulting is employed to carry out a hierarchical segmentation process. The hierarchical representation is as follow. First, we create the Region Adjacency Graph (RAG) [5]. This graph is used to represent the regions and their spatial relationships in the segmented image. Next, we iteratively merge neighbouring regions using similarity criterions like texture via Gabor filter [6] and spectral information via vegetation index (NDVI) and brilliance index (IB). Finally, the resulting segments are classified using the unsupervised k-means algorithm. The classification using this technique is usually realized at the level of pixels. We adapt this algorithm at the level of regions. We introduce the spectral and spatial attributes in the process of classification and use a new distance objects to centroids. We compute a confusion matrix, which is a good criterion to evaluate the quality of a classification. From there, we extract some performance indictors. These measures give us the optimal level of the hierarchy for which we have a better rate of good classification.

3. FEATURE CHARACTERIZATION

The heterogeneity of high resolution image complicates the extraction of the object reality. To significantly enrich the available information about the image and classification process, objects require a semantic formalisation of their characteristic. It is, thus, necessary to define series of indicators allowing this characterization.

3.1. Texture characterization

Texture is an important feature of images, it indicate the spatial distribution of the intensity values in the image and

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contains important information about the structural arrangement of surfaces.The extraction of texture features from high resolution remote sensing imagery provides a complementary source of data in which the spectral information is insufficient for identification of spectrally heterogeneous image. In recent years, the multichannel Gabor decomposition becomes very popular for texture analysis.In this paper, we propose the use of Gabor filter to extract texture features in high resolution satellite image.

3.1.1. Gabor filter Gabor filters are a group of wavelets. A set of filtered images is obtained by convolving the given image with Gabor filters. Each of these images represents the image information at a certain scale and at a certain orientation that cover appropriately the spatial frequency domain.

3.1.2. Texture feature extraction After applying the Gabor filter on the image, we compute an image of magnitudes or Gabor energies [6] in which pixel values are given by:

1,...,1,0;1,...,1,0

),(),(,−=−=

=

NnMm

yxGyxE mnnm (1)

Where m , n specify the scale and orientation of the generating function, respectively. M and N are the total number of scales and orientations, respectively.

The Gabor energy image is used to compute, for each region R , its feature vector f of size 2x M x N . A feature vector f is created using the mean mnμ and standard deviation mnσ of Gabor energy inside the region R :

[ ]4444,4343,4242,4141,1212,1111 ,,,,...,,, σμσμσμσμσμσμ=f (2)

3.2. Vegetation characterization

The spectral response of covered vegetal shows a high reflectance in the near infrared and a low reflectance in the red. The lower is the red values reflectance, the higher is the chlorophyll content. A relation between these two bands allows calculating indices of vegetation. Vegetation indices are used to isolate vegetation’s presence and photosynthetic activity from other types of land cover. The vegetation index that we used is the NormalizedDifference Vegetation Index (NDVI), which is defined by an algebraic formula:

RNIRRNIR

NDVI+−= (3)

Where NIR : Near Infrared Band And R : Red band

3.3. Ground characterization

This index translates the changes of brightness of the bare grounds. The index of brilliance (IB) is the most significant parameter that describes spectral behavior of the grounds. It is very sensitive to the brilliancy of grounds and is related to soil moisture and with the presence of salt on the surface. The index of brilliancy of grounds is calculated from the red and near infrared channels according to the following formula:

22 RNIRIB += (4)

4. HIERARCHICAL SEGMENTATION

After applying the watershed algorithm on the gradient image, we construct the RAG. In this representation, each node N represents one of the regions in the input segmentation. Each link in the graph that connects two adjacent fragments, i and j , is simply the watershed boundary that separates them. A region merging segmentation is based on the series of information mentioned previously: Texture, NDVI and IB features. At each step, two neighbouring regions are merged if the distance between their feature vectors are minimum compared with the other adjacent regions. The merging algorithm is described as below:

For each region Ri of the image do: For each adjacent region Rj do: - Extract texture feature - Extract NDVI feature - Extract IB feature - Consider the most similar adjacent region Rj to region Ri that satisfies the minimum distance similarity criterion. The similarity distance ),( ji RRS is given by:

),(/),(),( jiTjiAji RRSRRSRRS = (5) With:

AS : The measure of similarity based on spectral region attributes, it is defined in formula 6.

TS : The measure of texture feature similarity between the two regions, it is given by equation 7.

))(,)(max())(,)(min(

1),(1 jkik

jkikAk

kjiA RaRa

RaRaRRS −=

=

=

(6)

)( ik Ra : a spectral attribute of region Ri . A : The number of attributes used. We used here two

attributes: mean of NDVI and mean of IB inside the region.

−

+−===

))(,)(max())(,)(min(

1

))(,)(max())(,)(min(

1),(11

jmnimn

jmnimn

jmnimn

jmnimnN

n

M

mjiT

RR

RR

RR

RRRRS

σσσσ

μμμμ

(7)

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- Merge Ri and Rj into region Rk . - Compute the new features of the obtained region Rk .- Update the RAG. - Repeat the previous steps until there is no more merges.

5. CLASSIFICATION

For each level of segmentation, each region is a candidate object for classification. We use non supervised algorithm witch is k-means algorithm. The k-means clustering is well adapted to high resolution satellite image since different feature attributes can be used in the process of classification. This technique is usually realized at the level of pixels. The novelty of our algorithm consists in adapting it at the level of regions and in using a new distance between objects and centroids. The algorithm is the following: 1- Determine the number of classes. 2- Take random objects as the initial centroids. 3- Assign each object to the group that has the closest centroid based on minimum distance d . 4- Recalculate the positions of the K centroids. We repeat the third and the fourth steps until convergence (no object move group).

The distance d which we chose is based on the following steps. First, we look for two minimums from the spectral similarity distance AS between every region and centroids. Then, we affect objects to the one of both centroids, the one which has a minimal texture similarity distance TS .

( ) ( )( )jiA RRsCC ,min, 2min1min = (8) ( )( )2min1min ,min CCsd T= (9)With:

NNi 1= : The number of regions KKk 1= : The number of classes

1minC : The first centroid that is the closest in term of spectral similarity distance.

2minC : The second centroid that is the closest in term of spectral similarity distance.

The idea which we used removes a big ambiguity for objects having a similar spectral behavior. Indeed, the region is affected to the class which has the minimal texture distance.

6. EXPERIMENTAL RESULTS

The image we used in this work is a multispectral Ikonos image and it has four bands (Green, Red, NIR, green) as it is shown in figure 1. To evaluate the results, we compute a confusion matrix. Since we do not have ground truth, we defined a set of known data samples for every class predefined in the process of classification.

From there, a number of quality measurements can be derived such as the kappa coefficient K .

=++

=++

=

−

−

=M

iii

M

iii

M

iii

XXN

XXXN

K

1

2

11

*

*

(10)

With: iiX : The diagonal elements of the confusion matrix.

iX + : The sum of columns elements of the confusion matrix.

+iX : The sum of row elements of the confusion matrix. M : The number of classes. N : The total number of pixels.

6.1. Discussion First of all, we remark that for these three levels the bare soils are very well detected as it is shown in figures 2, 3 and 4. This result is due to the incorporation of the brilliance index IB in the both process (segmentation and classification). We mention also that vegetation areas are well detected. This is thanks to the NDVI index, but we must notice that we find confusion between the two types of vegetation in iteration 100 and 800 as it is shown in table 1 and table 3. Theses two classes share, necessarily, many similarities of texture and spectral features. For class boats, we observe that they are well classified in iterations 200 and 800 and this is due to the incorporation of texture attribute. We notice that the best result of classification for urban class is obtained in the level 100 with a little confusion between vegetation, bare soils and water classes as it is shown in table 1. From that, we can conclude that the quality of the classification depends strongly on the level of the hierarchy. In fact, from iteration to another, there is appearance and disappearance of objects. Classification accuracy is largely dependent on the precision of segmentation, i.e. the accuracy decreases when the segmentation implies errors which perturb the classification process as it is shown in figure 4 and proved by examine kappa coefficient ( )25.0=K . Finally, the classification results were considered satisfactory for the classes having a good kappa statistics. The optimum classification level was finally selected by observing the best kappa coefficient ( )86.0=K as it is shown in table 2.

Figure 1. Multispectral Ikonos image

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Figure 2. Segmentation and classification: iteration 100

Water Vegetation 1

Vegetation2

Bare soils

Boat Urban

water 35 0 0 0 0 0

Vegetation 1

0 152 494 0 0 0

Vegetation 2

0 11 189 0 0 0

Bare soils 0 0 0 187 0 0 Boat 4 0 0 0 26 0

Urban 4 0 11 2 0 116 K 0.47

Table 1. Confusion matrix: iteration 100

Figure 3. Segmentation and classification: iteration 200

Water Vegetation 1

Vegetation2

Bare soils

Boat Urban

water 35 0 0 0 0 0

Vegetation 1

0 646 0 0 0 0

Vegetation 2

0 38 162 0 0 0

Bare soils 0 0 0 187 0 0 Boat 0 0 0 0 30 0

Urban 0 0 11 64 0 58 K 0.86

Table 2. Confusion matrix: iteration 200

Figure 4. Segmentation and classification: iteration 800

water Vegetation 1

Vegetation2

Bare soils

Boat Urban

Water 35 0 0 0 0 0

Vegetation 1

0 0 646 0 0 0

Vegetation 2

0 36 164 0 0 0

Bare soils 0 0 0 187 0 0 Boat 0 0 0 0 30 0

Urban 0 20 0 64 0 49 K 0.25

Table 3. Confusion matrix: iteration 800

7. CONCLUSION

Segmentation is the main process and its aim is to create meaningful objects from images. The obtained results show the efficiency of the introduction of the various attributes in the process of classification to discriminate between the various classes. The classification quality depends strongly on the level of the hierarchy. To improve these results, it would be important to consider more complicate attributes: shapes, for example. As a perspective, another unsupervised classification algorithms could be used such as the Competitive Agglomeration (CA) algorithm, which has the advantage to automatically determine the optimal number of classes.

8. REFERENCES

[1] Rahim Aguejdad, Laurence Hubert-Moy, Philippe Clergeau. “Object-oriented image analysis for mapping urban expansion in western France”. In Proceedings of IGARSS 2006, July 31-August 04, 2006, Denver, USA.

[2] Ziyu WANG, Wenxia WEI, Shuhe ZHAO, Xiuwan CHEN. “Object-oriented Classification and Application in Land Use Classification Using SPOT-5 PAN Imagery”. In Proceedings of IGARSS 2004, Vol. 5, Sept 20-24, 2004, pp. 3158-3160.

[3] Hieu Tat Nguyen, Marcel Worring, and Rein van den Boomgaard. “Watersnakes: Energy-Driven Watershed Segmentation”. IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 25, No. 3, March 2003.

[4] Pietro Perona, Jitendra Malik. “Scale-Space and Edge Detection Using Anisotropic Diffusion”.IEEE Trans on Pattern Analysis and Machine Intelligence, Vol. 12. No. 7, July 1990.

[5] Kostas Haris, Serafim N. Efstratiadis, Nicos Maglaveras, and Aggelos K. Katsaggelos. “Hybrid Image Segmentation Using Watersheds and Fast Region Merging”. IEEE Transactions on Image Processing, Vol. 7, No. 12, December 1998, pp. 1684-1699

[6] Lianping Chen, Guojun Lu, Dengsheng Zhang. “Effects of Different Gabor Filter Parameters on Image Retrieval by Texture”. In Proceedings of Multimedia Modelling Conference, 2004. 10th International, Jan 5-7. 2004, pp. 273-278

Vegetation1

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Urban

Water

Vegetation1

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Bare soils

Boats

Urban

Water

Vegetation1

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