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J. Indian Soc. Remote Sens. (September 2009) 37:423–431

RESEARCH ARTICLE

Wavelet Frame-Based Feature Extraction Technique for Improving Classification Accuracy R.A. Alagu Raja . V. Anand . S. Maithani . A. Senthil Kumar . V. Abhai Kumar

Received: 15 April 2009 / Accepted : 8 May 2009

Keywords Wavelet frame transform . Texture classification . Unsupervised clustering . Accuracy assessment . Feature extraction

Abstract Classification of textures in remotelysensed data has received considerable attention during the past decades. One difficulty of texture analysis in the past was lack of adequate tools to characterize different scales of textures effectively.

R.A. Alagu Raja1( ) . V. Anand2 . S. Maithani3 . A.S. Kumar4 . V.A. Kumar5 1 Remote Sensing & GIS Lab, Thiagarajar College of Engineering, Madurai – 625 015, India 2 R & D Division, Tata Consultancy Services, Pune – 411001, India 3 HUSAD, Indian Institute of Remote Sensing, Dehradun – 248 001, India 4 National Remote Sensing Centre, Indian Space Research Organisation (ISRO), Hyderabad – 500 037, India 5 Thiagarajar College of Engineering, Madurai – 625 015, India

email : [email protected]

Recent space-frequency analytical tools like the wavelet transform can effectively characterize the coupling of different scales in texture and helps to overcome the difficulty. This paper presents a wavelet-based texture classification technique that was applied to a Multi-Spectral Scanner (MSS) image of Madurai City, Tamil Nadu, India The feature extraction stage of the technique uses Lemarie-Battle orthogonal wavelets to derive a texture feature vector and this vector is input to a fuzzy-c means classifier, an unsupervised classification procedure. Four indices (user’s accuracy, producer’s accuracy, overall accuracy and Kappa co-efficient) are used to assess the accuracy of the classified data. The experiment results show that the performance of the presented technique is superior to the classical techniques.

Introduction Texture analysis is a fundamental method for many applications in areas like remote sensing, digital

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imaging, quality inspection and medical imaging. Textures are replications, symmetries and combinations of various basic patterns usually with some random variation (Haralick, 1979). Although texture analysis has a long history, its applications to satellite sensor image data have been limited. Classification of textures in digital images has received considerable attention in the literature and a large number of approaches have been suggested. Texture classification is a process to extract features from a set of texture classes. In order to have an effective classification, features with good discriminatory details have to be obtained. Most of the existing approaches for texture feature extraction make use of statistical techniques in which processing the texture image data requires large storage space and computational capability. The scalar features calculated from the feature matrix may not be efficient to represent the characteristics of the image content (Laws, 1980). However, space-frequency domain methods can be attempted for extracting texture features. Recently, multi-scale filtering methods have shown significant potential for texture description, where advantage is taken of the spatialfrequency concept to maximize the simultaneous localization of energy in both spatial and frequency domains. Recent developments in wavelet transform provide a good multi-resolution analytical tool for texture analysis with high classification accuracy (Chang and Kuo, 1993). In this paper, a Lemarie-Battle wavelet frame based texture feature extraction technique followed by fuzzy c-means classification is presented. The background on feature extraction, the proposed methodology and experimental results are the different sections of the paper discussed.

Background on feature extraction techniques Transforming the input data into a set of useful details is called feature extraction. The goal of feature


extraction is to obtain a set of measures that can be used to discriminate different textures. The extraction of texture features from remotely-sensed imagery provides a source of data for identification or classification of spectrally heterogeneous landscape units. However, there is a wide range of texture analysis techniques that are used with different criteria for feature extraction: statistical methods and filter techniques. The basic assumption for most filtering approaches is that the energy distribution in the frequency domain identifies a texture. Some of the classical filtering techniques are discussed below. Laws masks One of the popular approaches for texture identification proposed by Laws was to use a bank of separable filters (Laws, 1980). The filter masks suggested consist of [1,4,6,4,1], [-1,-2,0,4,1], [1,0,2,0,-1], [-1,2,0,-2,1] and [1,-4,6,-4,1]. Feature vectors derived from Laws masks show good spatial discrimination and the measure is well localized. These masks, however, operate at a single image scale, thus reducing their capabilities to characterize underlying textural information at different resolution. Ring and wedge filters It is well known that the radial distribution of value in Fourier power spectrum(|F|2) is sensitive to texture coarseness in a picture f(x,y). A coarse texture will have high values of |F|2 concentrated near the origin, while in a fine texture the values will be more spread out. Thus, if one wishes to analyze the texture coarseness, a set of features that should be useful are the averages of |F|2 taken over ring-shaped regions centered at the origin, for various values of r, the ring radius. Similarly, it is well known that the angular distribution of values in |F| 2 is sensitive to the directionality of the texture in f(x, y). A texture with many edges or lines in a given direction θ will have high values of |F| 2 concentrated around the perpendicular direction θ +(π/2), while in a non-

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directional texture, |F|2 should also be non-directional. Thus the set of features for analyzing texture directionality should be averages of |F|2 taken over wedge-shaped regions entered at the origin for various values of θ the wedge slope. Here, seven dyadically spaced ring filters and four wedge-shaped filters were used for feature extraction. The filters are designed in the two-dimensional spatial frequency domain.

due to the size reduction of input image. This may be applicable for processing classical images. But for images like remote sensing data, minor information is also important to obtain higher classification rate. To achieve this, an algorithm making use of the discrete wavelet frame transform (DWFT) for satellite data classification is proposed in this paper.

Wavelet transform

Study area and data used

The wavelet transform is a bound alterable window method. A more accurate low frequency information can be produced using a longer interval and high frequency information using shorter interval. The advantage of the wavelet transform is information maintenance, which provides the space frequency information. Wavelet plays a central role in “Knowing what is where?”. The frequency content provides the information content about “what” and spatial co-ordinates provides the information about “where,” which is important for texture analysis. When used as a foundation for a texture measure, the wavelet transform enjoys several advantages such as spatial discrimination and multi scale representation over other methods (Coggins and Jain, 1985). The wavelet basis function is,

\

a, b

(X )

1 a

\(

x b ) a

(1)

Here, a, b are real. Each wavelet function in the decomposition is formed from a ‘mother’ wavelet function y(x) which is scaled (a) and translated (b) and the result being localized in both the frequency and spatial domains. The continuous time wavelet transform of a function f(x) is defined as, D

CTWT (a, b)

³D f ( x)

1 a

\(

xb )dx a

The multi temporal satellite sensor image used in this study is of Madurai city in Tamil Nadu, shown in Fig. 1 (a),(b) and (c). Madurai is the second largest city in the state of Tamil Nadu, after Chennai and is one of the oldest cities of India, with a history spanning over 2,500 years old. It is identified as one of the 12 heritage cities of India and it is situated between longitude 780 04’ 47" E to 780 11’ 23" E and latitude 90 50’ 59" N to 90 57’ 36" N. The topography of Madurai is approximately 101 meters above mean sea level. The land cover features of this study area include urban, vegetation, water body, waste land and hilly region. The scene details of the area are, Date of acquisition : 19th March, 2004 Satellite/Senor : IRS P6/ LISS III Resolution : 23.50 meter The scene was geometrically registered using Survey of India topographic sheets 58 K/1, in 1:50,000 scale and four 58 K/1 SW, SE, NW, NE in 1:25,000 scale were used as reference data for ground control points (GCPs) collection and validation. The standard errors were below 0.5 pixel along the X and Y directions. A sub area of 407×411 pixels were extracted from the original scale.

(2)

Most previous work on texture classification has focused on the discrete wavelet transform with decimation by which some of the information is lost

Methodology In this paper, we analyze the performance of the proposed feature extraction approach against the

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(a)

(b)

(c) Fig. 1 IRS P6 image of Madurai (a) Band 2 (b) Band 3 (c) Band 4.

classical feature extraction approaches which are used for classification of remote sensing images with different types of landscapes. Figure 2 shows the steps of the methodology for the proposed wavelet frame based feature extraction approach.

Computation of texture features In order to apply a wavelet frame to image texture analysis, the two-dimensional discrete wavelet transform is generated through the tensor product

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finest, which is highest frequency from the first level of the filter-bank and becomes progressively more coarse; it is this progression that allows the wavelet transform to provide a multi-scale representation of texture plot (Hiremath and Shivashankar, 2006). Texture classification

Fig. 2 Methodology.

of 1-D wavelets along the vertical and horizontal directions (Mallat, 1989). In quadrature mirror filters (QMF), this means the L and H filters are applied to the image in both the horizontal and vertical directions. The outputs are sub sampled by a factor of two to produce three high pass sub bands, HH, LH, HL and one low pass sub band LL. The process is repeated on the LL sub band to generate the next level of the decomposition using the same algorithm (DeBrunner and Kadiyala, 1999). If we do not downsample the low pass and high pass sub band images, we will get the DWFT solution for that image. DWFT has been investigated and applied to many fields. In DWFT, the down sampling operation is removed after the signal has passed through L and H filters. Thus, the size of the image remains unchanged in every processing stage. As a first step, DWFT is applied to the input image and we get the LL subband image that contains only low frequency information, but has the same image size as the original image plot (Chang and Jay Kuo, 1993). When applied to images the detail extracted is

Texture classification is a major task in the process of satellite sensor image analysis, which involves a method of identification of the different texture regions based on the corresponding features. The output from the texture feature extraction stage is a set of images. These images represent different features of the input image. They are used to form feature vectors, where each feature image corresponds to one element in the feature vectors. For the purpose of texture classification, a feature vector is needed to successfully characterize a textural region. When texture feature vector is used for classification there is heterogeneity within a texture in one class that causes spectral overlap with texture in other classes. So the traditional k-means clustering algorithm cannot handle data points that are close to more than one cluster. Using fuzzy logic the above problem can be overcome. One possible fuzzy clustering is to use fuzzy c-means clustering (Mecocci et al., 1995). The algorithm below is based on finding a good fuzzy partition of the data, which is carried out through an iterative optimization. a. b.

Choose primary centroids Ci (prototypes) Compute the degree of membership of all feature vectors in all the clusters § 1 · ¸ ¨

ª º ¨© q1 ¸¹ 1 « 2 » ¬« d ( x j , ci ) ¼»

uij

§ 1 · ¸ ¨

¦

k k

ª º ¨© q 1 ¸¹ 1 « » 2 1 «¬ d ( x j , cK ) »¼

(3)

428

c.


Compute new centroids

¦ ¦

M

ci

q

j 1 M

( u ij ) x j ( u ij ) q

(4)

and update the memberships, to step 2.

u ij to uˆ ij according

d.

k 1

If max ij u ij uˆ ij tol then stop, otherwise go to step 3.

It is important to note that the computation of the degree of membership u ij depends on the definition of the distance measure d2 (Xj , Ck ). Accuracy assessment Analysis of confusion matrix has been one of the most common means of expressing classification accuracy. Error matrices compare, on a category by category basis, the relationship between known reference data (ground truth) and the corresponding results of an automated classification. Various accuracy indices such as overall accuracy, producer

Fig. 3 Classified image - Laws Masks.

accuracy, user accuracy and kappa coefficient (Lillesand et al., 2004) have been calculated using the field visit data in determining the accuracy of classification. Commonly, most of the researchers recommended the use of the Kappa coefficient of agreement as the standard measure for accuracy (Fung, 1990).

Experimental results To demonstrate the effectiveness of the proposed technique, we have used Lemarie-Battle wavelet to extract the features from remotely sensed data of Madurai City for texture classification. Also we have implemented the Laws mask and Ring and wedge based texture extraction techniques for comparative analysis. Figures 3 to 5 depict the classification results for the two classical techniques and the proposed wavelet frame-based technique respectively. Even by a visual analysis of the Figs. 3 and 4, it is clear that the features were significantly


Fig. 4 Classified image - Ring and wedge.

Fig. 5 Classified image - Wavelet Frames.

429

430


misclassified with respect to the classification results obtained with the proposed approach. Table 1 summarizes the quantitative results obtained by comparing the reference map (through field visit) with the classified map yielded applying the proposed and traditional feature extraction techniques. The performance analysis of these techniques have been carried out in terms of overall, producer, user and kappa accuracy indices. The traditional Laws masks and Ring and Wedge filters produced the classification accuracies (in terms of Kappa coefficient) about 0.72 and 0.75 respectively, whereas the wavelet frame based technique produced acceptably high accuracy (0.88 Kappa). This is possible due to the spatial-frequency nature of wavelet frames which maximizes the simultaneous localization of energy in both the spatial and frequency domains. Also, the proposed approach extracts texture features by focusing on the dominant frequency regions whereas the traditional approaches fail to do so.

of wavelet frame transform is higher. This can be attributed to the fact that the wavelets analyze the textures at multiple resolutions of the image. Since many textures in remotely-sensed data have dominant frequency in middle frequency channels, the wavelets, which are capable of focusing in the dominant frequency region provides good spacefrequency information, which gives unique texture signature that leads to higher classification accuracy. Further research is underway to analyze the potential of this method for texture analysis of high spatial resolution multispectral data obtained after merging with panchromatic data.

Acknowledgements The authors are thankful to the Indian Space research Organization, Department of Space, Government of India for providing financial assistance under RESPOND Scheme to carry out the research.

Table 1 Assessment of classification accuracy Accuracy indices Technique

Overall (%)

User (%)

Producer (%)

Kappa

Laws mask

76.30

68.17

70.38

0.8192

Ring & wedge

79.60

69.96

73.83

0.8463

Wavelet frames

87.60

80.23

77.45

0.9042

Conclusion

References

This paper provides a wavelet frame-based feature extraction technique combined with fuzzy c-means clustering for texture information extraction from multispectral satellite data and the comparison is made with classical techniques like Laws mask and Ring and Wedge filters. The results found from the experiment demonstrate the higher performance of the proposed approach. When processing aperiodic function like remote sensing images, the efficiency

Chang T and Jay Kuo CC (1993) Texture analysis and classification with tree structured wavelet transform. IEEE Transactions on Image Processing 2(4): 429– 441 Coggins JM and Jain AK (1985) A Spatial Filtering Approach to Texture Analysis. Pattern Recognition Letters 3(3): 195–203 DeBrunner Victor and Kadiyala Madhavi (1999) Effect of Wavelet Bases in Texture Classification using a TreeStructured Wavelet Transform. IEEE:0-7803-5700-0/99

J. Indian Soc. Remote Sens. (September 2009) 37:423–431 Fung T (1990) An assessment of TM imagery for land – cover change detection. IEEE Transactions on Geoscience and Remote Sensing 28(4): 681–684 Haralick M (1979) Statistical and structural approaches to texture. Proceeding of the IEEE 67(5): 786–804 Hiremath PS and Shivashankar S (2006) Texture classification using wavelet packet decomposition. Int. J. Graph., Vis. and Image Processing 6(2): 77–80 Laws KI (1980) Rapid Texture Identification. Proc. SPIE Conf. Image Processing for Missile Guidance, pp 376–380

431 Lillesand MT, Ralph Kiefer W and Jonathan Chipman W (2004) Remote Sensing and Image Interpretation, 5th edition, Wiley International edition pp 586–592 Mallat SG (1989) A Theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11: 674–693 Mecocci A, Gamba P, Marazzi A and Barni M (1995) Texture segmentation in remote sensing images by means of packet wavelets and fuzzy clustering. In Proc. of the European Symposium on Satellite and Remote Sensing II, volume SPIE 2584, pp 142–157