Combining Pixel- and Object-based Approaches for Multispectral Image Classification using Dempster-Shafer Theory

Youcef Brik, Nabil Zerrouki and Djamel Bouchaffra Learning Patterns for Recognition and Actuation (LEAPRA) Laboratory

Center for Development of Advanced Technology (CDTA) Baba Hassen, Algiers, Algeria

ybrik@cdta.dz, nzerrouki@cdta.dz and dbouchaffra@ieee.org

Abstract—We propose an efficient framework for combining pixel and object-based approaches for Remote Sensing Image Classification using Support Vector Machines (SVMs) and Dempster-Shafer Theory of Evidence (DSTE). The pixel-based technique employs the multispectral information for assigning a pixel to a class according to the spectral similarities between the classes, and the object-based technique operates on objects consisting of many homogeneous pixels grouped together in a meaningful way through image segmentation. In order to manage the conflict that results from using both approaches, the final decision is performed using DSTE’s rule combination based on probabilistic output from both SVM classifiers. The evaluation test conducted on ETM+ image of Landsat-7 shows that the proposed technique achieved 95.24% classification accuracy. This performance is 5.78% higher than the better accuracy obtained by both SVMs. The proposed combination framework outperforms traditional methods by 2.14% accuracy’s margin.

Keywords:Remote sensing image classification; pixel-based approach; object-based approach; support vector machines; dempster-shafer theory of evidence.

I. INTRODUCTION Remote sensing images have proven to be very useful

in land cover mapping, climate studies, forest and harvest inventory and environmental monitoring. Since remotely sensed images consist of rows and columns of pixels, the process of assigning each pixel to one thematic map class is named image classification [1]. Several techniques for remote sensing image classification have been proposed in the literature [2–4]. However, two types of classification techniques emerge as the building blocks: the first one is pixel-based image analysis (based on a per-pixel basis) [5] and the second one is object-based image analysis (based on objects or segments) [6].

Pixel-based classification explores the multispectral information for assigning a pixel to a class according to the spectral similarities between the classes. The most used methods for pixel-based classification are the Maximum Likelihood Classification (MLC) [7] and Iterative Self-Organizing Data Analysis Technique (ISODATA) [8]. The strength of the pixel-based approach remains in its ability to operate directly on individual pixels to identify their classes by comparing their feature vector with each class model. Usually, the feature vectors consist of gray-level values of a pixel from multispectral bands and some contextual information. However, the pixel-based approach doesn’t take into account spatial information and ignores the relationship between the real

world and the classified image with high resolution [9]. In other words, this approach is limited, because it exploits only spectral information without considering contextual and textural information [5,10]. Furthermore, the pixel-based approach encounters some difficulties in extracting objects of interest [9].

Unlike single pixel-based classification, object-based approaches operate on objects consisting of many homogeneous pixels grouped together in a meaningful way through image segmentation [11]. In addition to spectral and textural information used in pixel-based classification methods, image objects highlight shape characteristics needed for classification [12].

Segmentation of remotely sensed images is a difficult task due to mixed pixels, spectral similarity and the textured appearance of many land-cover types [13]. Many segmentation algorithms are based on a region-growing approach where pixels are iteratively grouped into regions based on some predefined similarity criteria. Some examples of region-growing approaches can be found in [14]. Likewise, there are several methods which can be deployed for image segmentation. Examples of these methods are: watershed transformation [15], fuzzy c-means [16], level set [17] and mean-shift image segmentation [13].

The spectral, spatial and textural features have been extracted to improve the performance of object classification [12,18]. Finally, all the features are used to classify the land cover in the area of study. The classifier estimates the similarity between an unclassified image object and all template images [15]. The class assigned to the nearest template is selected.

Object-based classification approach offers a high accuracy and smoother classification map over pixel-based classification in the case of high spatial resolution imagery [19]. However, a potential limitation of object-based classification is due to the problem of under-segmentation. On the one hand, errors due to under-segmentation cannot be adjusted in the unit of object and therefore affect substantially the classification accuracy. On the other hand, pixel-based contextual classification uses pixel neighbor’s information to reduce salt-and-pepper noise and does not encounter segmentation issues. Thus, one of the main challenges of pixel-based contextual classification is to determine the appropriate contextual neighbors.

Recent advances in classification task have shown that Support Vector Machines (SVM) outperform most of the traditional classification algorithms [20]. This classifier is effective in high-dimensional data with small size training sets [7].

2013 International Conference on Signal-Image Technology & Internet-Based Systems

978-1-4799-3211-5/13 $31.00 © 2013 IEEE

DOI 10.1109/SITIS.2013.79

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Several comparisons between the pixel-based and object-based image results have been conducted [21-23]. Nevertheless, there were no references which studied the combination of these approaches by Dempster-Shafer theory of evidence (DSTE). In this paper, we present a new classification approach that combines advantages of both pixel and object-based approaches. Individual image pixels are first classified by a multiclass SVM with a radial-basis function (RBF) spherical kernel using spectral information. The same image is segmented and textural, spectral and contextual features are generated from the segmented image. Furthermore, a second multiclass SVM is applied to assign each object to the corresponding class. Since the classification results of both approaches are different, a consensus function is needed. We fall back on DSTE [24] to determine an agreement between both classifiers. In order to improve the recognition rate, DSTE combines seamlessly the outputs of both SVM classifiers by solving the conflict generated at every pixel. The proposed work blends the benefits of both approaches, where object-based information is added to the pixel-based classification. Therefore, additional information that was not available in the pixel-based classification is exploited to improve the land cover classification accuracy.

II. STUDY AREA AND DATA DESCRIPTION The data used in this work is multispectral image

acquired on June 2001 by ETM+ sensor of Landsat-7 satellite.

Figure 1. False-color composite of study area using bands 2, 3, and 4 from ETM+ sensor.

It contains six multispectral bands namely: blue (band

1), green (band 2), red (band 3), near-infrared (band 4) and mid-infrared (bands 5 and 7) portions of the electromagnetic spectrum with a spatial resolution of 30 meters. The subset of Landsat-7 remotely sensed images is selected from the area of Algiers, which is located in the north of Algeria (Coastal area). Exactly, this image covers the north-eastern part of Algiers, including the international airport and many urban cities. It is about 400x400 pixels, covers approximately 244 km2 and represents a complex nature/urban environment. As the linkage between the vegetation and the urban, the dominant land-use-land-cover types in this area include built-up areas, agricultural and green land. The false color imagery is depicted by Fig.1.

III. PROPOSED METHODOLOGY In this section, we provide more details about the

proposed methodology. We first present pixel-based approach, and then object-based approach. In order to improve the classification accuracy, SVM-DSTE combination is presented. The entire architecture of the system used in this study is illustrated by Fig.2.

A. Pixel-based Approach Per-pixel approaches identify the class of each pixel

separately in the image by comparing the n-dimensional feature vector of each pixel with the prototype vector of each class using pixel's spectral information. The feature vectors consist of pixel’s gray level values from multispectral bands. The classical clustering algorithms (e.g., K-means, ISODATA, K-Nearest Neighbors, maximum likelihood, minimum distance to the mean, and Mahalanobis distance) whether supervised or unsupervised have been widely used [2]. These algorithms are revealed not very effective to perform pixel classification [3]. This is one of the reasons that pushed us to select the SVM classifier, whose goal is to determine the location of decision boundaries that produce the optimal separation between classes [25].

B. Object-based Approach The basic elements of this approach are image objects.

The object-based method contains three major parts which are: image segmentation, object feature extraction and object classification.

1) Image object segmentation: it consists of dividing an image into a number of homogeneous segments, which is the premise for object-based classification. The result of image segmentation directly impacts the performance of a subsequent classification. Watershed technique newly-used in remote sensing image segmentation produces one-pixel wide, closed and accurately localized contours with a low computational complexity [12]. Watershed transformation of an image is a mapping from the original image to a labeled image such that all points in a given catchment basin have the same unique label. With the marker image generated from the previous stage, the split and merge fast watershed algorithm developed by Dobrin et al. [27] is applied to the image.

2) Object feature extraction: This step consists of capturing the spectral, shape and textural features. Some frequently-used shape and textural features are given in [6]. The extracted features are based on image segmentation. The spectral features contain brightness, mean, standard deviation, soil adjusted vegetation index (SAVI), the normalized difference vegetation index (NDVI) and the normalized difference water index (NDWI). The shape features are area, length, main direction and shape index. The main texture feature is entropy which is calculated in several bands (red, green, blue, and near infrared). In our work, the object feature descriptor is composed of 29 features which are the values of spectral attributes (computed for each band: brightness × 6, mean × 6, standard deviation × 6, SAVI, NDVI and NDWI), texture attribute (entropy × 4), and finally the spatial attributes are computed (area, length, shape index and form factor).

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Figure 2. The proposed methodology flowchart.

3) Object classification: In order to assign classes to

segmented objects, the classification process consists of two steps: (i) give the system certain image objects as training samples, and (ii) classify image objects according to their feature spaces via a trained classifier model. Hence, several classifiers have been used in remote sensing [2, 6]. In this work, we have selected an SVM classifier due to its high generalization performance even with a limited training set.

C. Support Vector Machines In order to identify the class associated with each

pixel/object, we use Support Vector Machine (SVM) to perform supervised classification on remotely sensed image. SVMs are a set of powerful learning methods that perform classification based on statistical learning theory [25]. SVMs were first designed to solve binary classification. They can function as a multiclass classifier by combining several binary SVM classifiers for each possible pair of classes.

SVM can be adapted to behave as a nonlinear classifier through the use of nonlinear kernels. Unlike other state-of-the-art classifiers, SVM has the advantage of solving sparse sampling, nonlinear, high-dimensional and global optimum problems (via a convex optimization). A comprehensive review and a detailed description of the SVM technique can be found in [20]. In our case, SVM classification output is the decision values of each pixel/object for each class using LIBSVM

package [26], which are used for probability estimation to perform DSTE decision rule.

D. Dempster-Shafer Theory Dempster-Shafer theory of evidence (DSTE) [24]

establishes a frame of discernment, which is defined as a set of mutually exclusive and collectively exhaustive hypotheses denoted by�� . DSTE includes modeling of both imprecision and uncertainty through the definition of two functions: plausibility (pls) and belief (Bel). Both of them are derived from a mass function (m). This latter function applies to all elements of �� rather than to only elements of��. This fact constitutes a flexible and a rich modeling covering many situations in remote sensing. Then, a mass function m(T) assigns belief committed to each proposition, as shown:

�� � � �� ����������� � ��������������������� � � ��� � �� ���� � ����

(1)

where � is the empty set. Based on the mass function, belief (Bel) and plausibility (Pls) are two other common evidential measures, which are defined, respectively, as:

� !�"� � � ������# $%& (2) '!(�"� � � ����)*$%& , (3)

where, for every " � +, Bel(S) is a measure of the total amount of beliefs committed exactly to every subset of S by m. Pls(S) represents the degree to which the evidence remains plausible. These two functions, which are regarded as the lower and upper probabilities, respectively, have the following properties:

� !�"� , '!(�"� (4) '!(�"� � � - � !�"�. (5)

Dempster’s rule of combination in the mathematical theory of evidence can combine the mass functions mi obtained from n sources of information as following:

��"� � �.�".�/��0�"0�/1/��2�"2� (6)

��"� � 3 4�5�"5�2

56.)7*1*)86)�� - 9�: � ������������;�

where K represents the degree of conflict given by:

9 � 3 4�5�"5�2

56.)7*1*)86<��=�����������������������>�

Several ways of taking the final decision using DSTE decision technique such as the maximum mass, plausibility or belief hypothesis have been proposed in the literature [28]. From the probabilistic SVM classifiers, the posterior probabilities are converted in the form of mass function (m) which are then combined using DSTE rule combination. The combination output can be considered as a belief function defining for each thematic a posterior probability measure.

Remotely Sensed Data

Pixel-based image analysis

Object-based image analysis

Feature extraction from pixel data samples

Feature extraction from object data samples

Pixel-based SVM classification

Object-based SVM classification

Accuracy assessment for each approach

Probabilistic SVM output normalization

Probabilistic SVM output normalization

Combining pixel and object-based approaches with DSTE

Final classified image and performance assessment

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IV. EXPERIMENTAL RESULTS

A. Experimental Protocol 1) Dataset: The remotely sensed image used in this

work is a quadrangle area with 6 generated bands and has 5 thematic classes which dominate the study site namely: Vegetation (V), Water (W), Dense Urban (DU), Less Dense Urban (LDU) and Bare Soil (BS). We manually denoted the ground truth of data samples for 5 classes. The total number of selected samples is 2633 pixels to evaluate both approaches as well as the combined system.

2) Evaluation: A confusion matrix is useful when assessing the classification accuracy of one or all categories. The confusion matrix is derived from a comparison of reference image pixels to the classified image pixels. Another measure which can be extracted from a confusion matrix is the kappa coefficient. The motivation of this measure is to estimate agreement in different classes. Thus, this coefficient is calculated as:

9?@@?�AB CDED FG � '�H� - '�I�� - '�I� �� �����������������J� where P(D) is the percentage of correct classified instances and P(E) is the sum of the multiplication of the marginal probabilities per class divided by the sum of total instances.

To evaluate the proposed method, 3-fold cross-validation has been implemented. However, the original sample for every class is randomly partitioned into 3 sub-samples. Of the 3 sub-samples, a single sub-sample is used for testing, and the remaining 2 sub-samples are used for training. This process is then repeated for 3 folds, with each of the 3 sub-samples used exactly once. Finally, a single value results from averaging all.

3) Results:

In the pixel-based classification, the spectral features are the only attributes that have been used. For the object-based classification, we have first evaluated a segmentation step using watershed technique. Following the image segmentation process, features were carefully generated to describe accurately the image objects. The total number of object features available to the object-based classification is more than the number of features used in pixel-based classification (29 versus 6, respectively).

Using the whole image, the image objects produced at the segmentation phase and the ground-truth image, a stratified random sample of image objects within the 5 land cover classes was performed. A total of 500 image objects were selected (100 objects per class). These samples were then split into training and testing sets using 3-fold cross-validation technique. In order to ensure that both the pixel and object-based classifications used training and testing data gathered from the same locations, each point within the selected image object would correspond to the land cover class already identified for the pixel-based analysis.

In order to highlight the significant difference between pixel- and object-based classifications statistically, we used the same machine learning algorithm based on SVMs with the radial basis function (RBF) kernel.

TABLE I. PIXEL-BASED CLASSIFICATION RESULTS.

Reference data V W DU LDU BS

Cla

ssifi

ed

data

V 85.75 0.22 3.75 2.07 10.72 W 0 98.63 0 0 0 DU 0.18 0 80.83 7.98 0.07

LDU 5.18 1.09 15.35 88.67 1.44 BS 8.89 0.06 0.07 1.28 87.78

Kappa Coefficient =0.85, Overall Accuracy = 88.24%

TABLE II. OBJECT-BASED CLASSIFICATION RESULTS.

Reference data V W DU LDU BS

Cla

ssifi

ed

data

V 90.71 2.83 0 0.16 4.58 W 0.1 94.46 0 0 0 DU 0 0 84.95 11.78 0.27

LDU 0.55 1.55 14.45 85.63 3.56 BS 8.64 1.16 0.60 2.42 91.59

Kappa Coefficient = 0.87, Overall Accuracy = 89.46%

TABLE III. COMBINATION RESULTS.

Reference data V W DU LDU BS

Cla

ssifi

ed

data

V 92.46 0 0 0 0 W 2.73 100 0 0 0 DU 0 0 90.79 0 0

LDU 0 0 7.97 100 0 BS 4.79 0 0.61 0 100

Kappa Coefficient =0.93, Overall Accuracy = 95.24%

The two parameters for SVM models using the RBF kernel are “cost” (C) and “sigma” (�). In order to select the best pair of these parameters in each SVM classifier, we have iteratively tested different values of C and �. For each classification, a confusion matrix is presented in table I, II and III, along with their overall accuracy and kappa coefficient.

B. Results Interpretation The classification accuracies achieved by pixel-,

object-based and combined method were 88.24%, 89.46% and 95.24% respectively. The pixel-based classification result is shown on Fig.3-a. The statistical assessment of this result gives a confusion matrix of Table I. We can see that the DU class is confused with the LDU class (15.35%). This confusion is due to the degree of similarity of spectral features between the two classes. In addition, the V is greatly confused with BS (10.72%) which is caused by the similarity of spectral response of both classes.

The results of the object-based classification are depicted in Table II and shown in Fig.3-c, from which we can see that W and V are classified very well. This result is due to the incorporation of the NDWI and the NDVI indexes in the classification process. However, we should notice that we find misclassification between DU and LDU with 14.45% of DU testing pixels misclassified as LDU, and 11.78% of LDU testing pixels misclassified as DU. This is caused by the over segmentation observed on some objects where the structural and spatial features of urban areas are not discriminate.

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Table III summarizes the confusion matrix obtained by

the combination method. One can notice that the average accuracy is much better than both approaches reported previously. The W, LDU and BS classes are perfectly accurate with 100% of their reference pixels. The V class accuracy is increased by almost 1.75% when using DSTE combination compared to the best one of both approaches (see Fig.3-b). Misclassifications between the DU and LDU classes are the largest source of error with 7.97% of DU testing pixels misclassified as LDU.

Accordingly, the Table III results show that the combination of both approaches contributes to the identification of all classes and each provides complimentary information. Thanks to DSTE decision rule, the conflicts generated from both approaches have been managed. It blends the benefits of both approaches, where object-based information is added to the pixel-based classification.

C. Comparaison We performed a comparison between the proposed

combined system and a set of well-known combination methods such as Majority voting, Decision Template, Average and Bayes. These techniques reported in [29, 30] are considered to be the best among all fusion techniques in pattern recognition. Because of that, there is a need to find out the effectiveness of the combined framework proposed in this work. The overall accuracy for the four combination methods as well as the proposed system is given in Table IV below.

TABLE IV. COMPARISON RESULTS

Combination method

Majority voting Decision Template Average Bayes DSTE

Accuracy (%)

90.48 92.24 92.05 93.10 95.24

From Table IV, it can be seen that our approach gives

higher classification accuracy providing an improvement of 2.14% over Bayes technique and 3% compared to decision template technique. However, the performance of the average technique is above than 92%, which is better than majority voting combination.

Consequently, with the obtained results we infer that

our methodology (i.e., combining pixel and object-based classification using DSTE) performs better than one of both approaches as well as the well-known combination methods.

V. CONCLUSION AND FUTURE WORK In this work, we have presented a combination of

pixel- and object-based approaches for land-cover image classification using SVM and the DSTE combination method. DSTE process merged the power of each classifier to derive a more powerful classifier. DSTE has shown more improvement than each SVM classifier separately. Furthermore, it is worth noticing that the improvement margin of the DSTE is up to 5%. This success is justified by the complementary contributions of both pixel- and object-based approaches. In fact, we have extracted spectral and contextual information from pixel-based method and textural and shape information using objetc-based method. Our future work consists of combining the two SVM classifiers using hyper-spectral data which covers a wide range of information.

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