Classification of Multispectral High-Resolution Satellite Imagery Using ...

Classification of Multispectral High-Resolution Satellite Imagery Using LIDAR Elevation Data María C. Alonso and José A. Malpica Dpto. de Matemáticas Escuela Politécnica. Universidad de Alcalá de Henares, Spain {mconcepcion.alonso,josea.malpica}@uah.es

Abstract. This paper studies the influence of airborne LIDAR elevation data on the classification of multispectral SPOT5 imagery over a semi-urban area; to do this, multispectral and LIDAR elevation data are integrated in a single imagery file composed of independent multiple bands. The Support Vector Machine is used to classify the imagery. A scheme of five classes was chosen; ground truth samples were then collected in two sets, one for training the classifier and the other for checking its quality after classification. The results show that the integration of LIDAR elevation data improves the classification of multispectral bands; the assessment and comparison of the classification results have been carried out using complete confusion matrices. Improvements are evident in classes with similar spectral characteristics but for which altitude is a relevant discrimination factor. An overall improvement of 28.3% was obtained, when LIDAR was included. Keywords: LIDAR; Satellite Imagery; Classification, Support Vector Machine.

1 Introduction Every day, spatial and spectral satellite imagery resolutions improve and become cheaper and easier to acquire. A single image from a high-resolution satellite provides a huge amount of information that needs to be reduced for analysis and interpretation. Currently, the interpretation of high-resolution satellite images is carried out “manually” by visual interpretation; this is because traditional classification algorithms for low resolution are too limited in dealing with the complexity of high-resolution data. Segmentation-based classifiers can overcome the limitation imposed by the huge amount of information to some degree, by dividing images into homogenous segments and using them as a basis for interpretation or further classification procedures. Several authors have studied classification methods for satellite images [1] and [2]. The combination of LIDAR (Light Detection And Ranging) with multispectral imagery can significantly improve classification, both in terms of accuracy and automation. LIDAR provides the altitude necessary for discriminating between certain classes, but is blind to the type of object it is measuring, while the multispectral data provides intensity and facilitates the identification of the type of object. In this paper, we will show the benefits of synergistically merging sensors for mapping buildings and for urban vegetation. The automatic extraction of buildings and vegetation in urban areas can be used in the important application of updating urban inventories used to monitor urban vegetation and to assess buildings or vegetation changes. G. Bebis et al. (Eds.): ISVC 2008, Part II, LNCS 5359, pp. 85–94, 2008. © Springer-Verlag Berlin Heidelberg 2008

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Several authors have combined multispectral and LIDAR data. Syed et al. [3] consider object-oriented classification to be superior to maximum likelihood in terms of reducing “salt and pepper”. Alia et al. [4] describe an automated procedure for identifying forest species at the tree level from high-resolution imagery and LIDAR data. The results show an improvement in the ability to discriminate between tree classes. An improvement in the classification of Ikonos imagery when integrated with LIDAR is shown in Zeng et al. [5]; this integration of LIDAR with multispectral data has been shown to be beneficial for the detection of buildings [6], the extraction of roads [7] and the classification of costal areas [8]. The integration of multispectral imagery and multireturn LIDAR for estimating attributes of trees was reported in [9]. Our work combines LIDAR elevation data and SPOT5 multispectral data for the classification of urban areas. We have used the Support Vector Machine (SVM), and our results confirm those of the above works by providing other data and classification algorithm.

2 Materials and Study Area To determine the method's potential, a SPOT5 satellite has been used. The SPOT5 satellite was launched in 2002 and captures panchromatic images with a resolution of 2.5 m, and multispectral images with a resolution of 10 m (for the bands R, G and NIR) and 20 m (for the MIR band). The imagery for our experiment was captured at 11:20 a.m. on 12 August 2006; it represents an area of approximately 4×2 km2 southeast of Madrid, Spain, on a mostly flat terrain. The azimuth and elevation of the sun were 150.07 and 62.37, respectively (Fig. 1).

Fig. 1. The study area in a false color SPOT image generated with bands 1, 2 and 3 for the RGB monitor channels. The image shows Alcala University Campus and surrounding areas, situated in the southeast of Madrid, Spain.

Classification of Multispectral High-Resolution Satellite Imagery

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Fig. 2. LIDAR elevation image of the same area as Fig. 1

3 Support Vector Machine Classifier For the clarity of the paper, a brief description is given of the algorithm used for the classification, the Support Vector Machine (SVM); more detail can be found in [10]. The idea for SVM initially appeared in an article by Boser et al. [11], where they applied it to optical character recognition problems. They demonstrated the superior generalization of SVM as compared with other learning algorithms. SVM maximizes the margin between the training patterns and the decision boundary. To illustrate the algorithm, let us start with a two class problem. Given some training data by a set of point of the form

{

}

T = ( xi , zi ) xi ∈\ n , zi ∈{−1,1}

i =1... m ,

(1)

where xi is a point of an n-dimensional feature vector of the training set, which has cardinality m, and zi is a label indicating the class to which the element xi belongs, either 1 or -1 (as mentioned above, this is a two class problem). The set of points x satisfying the equation g ( x) ≡ w x + b = 0

(2)

form a hyperplane of Rn, where the vector w is perpendicular to the hyperplane, and b − represents the offset of the hyperplane form the origin along the vector w. w All of the training points will lie on one or the other side of the hyperplane g(x); ⎧ ( w⋅ xi − b ) > 0 ⎨ ⎩( w⋅ xi − b ) < 0

if zi = 1 if zi = −1

.

(3)

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There are infinite hyperplanes that separate the two classes in training points. Let us select the hyperplane that maximizes the perpendicular distance to the points nearest to the hyperplane on both sides. Scaling w by a constant and fixing b appropriately, the following equations for the parallel bounding hyperplanes can be obtained ⎧ ( w⋅ xi − b ) ≥ 1 ⎨ ⎩( w⋅ xi − b ) ≤ −1

if zi = 1 . if zi = −1

(4)

In order to express this as only one equation, equation 4 can be rewritten as:

zi ( w⋅ xi − b ) ≥ 1 .

(5)

g(x)=1 g(x)=0 g(x)=-1

support vectors

2 w

Fig. 3. Support Vector Machine for linearly separable data

The objective is to find the separating hyperplanes with the largest gap or margin. This objective is accomplished when we find the weight vector w that maximizes the distance between the g(x)=1 and g(x)=-1 hyperplanes. Basic geometric calculation 2 . Therefore, maximizing tells us that the distance between these hyperplanes is w the hyperplane margin will be the same as minimizing w , i.e. minimize:

1 2 w , subject to zi ( w⋅ xi − b ) ≥ 1 ∀i , i = 1..n . 2

(6)

This can be now resolved through quadratic programming techniques. The support vectors are the training samples that define the optimal separating hyperplanes, i.e. the training elements for which equation 4 becomes an equality (Fig. 3). By the Lagrange method of undetermined multipliers, the problem expressed in equation (6) can be reformulated as:


f ( w, λk ) =

n 1 2 w − ∑ λk ( zk wt xk −1) . 2 k =1

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(7)

We seek to minimize f with respect to w, and maximize it with respect to λk. So far, we have dealt with only the two class problem. SVMs are basically binary classifiers by their inherent nature; however, they can be redesigned to handle the multiple classification problems that commonly arise in remote sensing applications. The two approaches commonly used to do this are the One-Against-All and the OneAgainst-One techniques. No superiority of one method over the other has been reported so far [12]. The generalization to a multiclass problem is achieved by calculating c linear discriminant functions. It has been reported by Mercier and Lennon [13] that high classification accuracy can be accomplished with small training sets. The SVM provides a mathematical foundation for how well the classifier will generalize to unseen data. The SVM training algorithm is guaranteed to converge to the globally optimal. Furthermore, SVM classifier can learn highly non-linear discrimination functions. Several authors have applied SVM to images; for instance, Azimi-Sadjadi and Zekavat [14] used SVM to classify ten different cloud and cloudless areas. The SVM has been compared to other classification methods for remote sensing imagery such as Neural Networks, Nearest Neighbor, Maximum Likelihood and Decision Tree classifiers, and have surpassed them all in robustness and accuracy [15]-[18]. These results lead us to apply SVM instead of others classifiers. We have applied it with the Radial Basis Function kernel for a gamma parameter of 0.2.

4 Classification of SPOT5 with LIDAR With the four multispectral bands and the panchromatic image, a multispectral four bands pansharpening is performed with PCA [19] (Fig. 4). The SPOT5 multispectral pansharpening images and the LIDAR (after resampling and registration [20] (Fig. 4)), data are integrated and treated as independent multiple band imagery in order to carry out the classification. First, a k-means unsupervised classification was performed over the pansharpened multispectral bands, with and without the LIDAR layer, in order to determine the spectral and mixed (spectral plus elevation) characteristics of the data. Eventually, a class arrangement of five classes was established: high vegetation (trees), low vegetation (shrubs and lawns), natural grounds, artificial grounds and buildings. With the help of aerial photos and ground visits, samples for training and evaluating the classifier were selected from the SPOT-LIDAR merged data set. Since the study area corresponded to the University Campus of Alcala and surrounding area, many visits were feasible. Several provisional numbers of classes and training samples were tested for the subsequent supervised classification and quality assessment, following the methodology described in [2]. The final distribution of the classes can be seen in Table 1.

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M.C. Alonso and J.A. Malpica Table 1. Class Distribution Summary

Trees Shrubs & lawn Natural ground Artificial ground Buildings Total

Training sample Percent Points 552 6.56 646 7.67 3156 37.48 1080 12.83 2986 35.46 8420 100

Test sample Points Percent 558 5.83 738 7.71 4028 42.06 2880 30.08 1372 14.33 9576 100

Input Panchromatic 2.5 m R, G, NIR, MIR 10, 10, 10, 20 m

LIDAR 1 m

Resampling and Registration Pansharpening R, G, NIR, MIR, LIDAR, 2.5 m

R, G, NIR, MIR 2.5 m

Training sample Supervised Classification by Support Vector Machine Test sample Classified image, accuracy 71.23%

Classified image, accuracy 99.50%

Output Fig. 4. Flow chart for the classification


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5 Results and Discussion Fig. 5 and Fig. 6 show the results of the SVM classifications without and with LIDAR, respectively.

Fig. 5. Classified image without LIDAR band

Fig. 6. Classified image with LIDAR band

One thing that is immediately apparent when comparing both classifications is the differences in building extraction; in Fig. 5, buildings appear fuzzier and wrongly delimited in relation to Fig. 6. Some areas of the central motorway, which goes from left to right in the image, have been confused with the building class, as can be

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M.C. Alonso and J.A. Malpica Table 2. Confusion matrix for classification without the LIDAR band

Ground Truth Class (Pixels)

Shrubs & Natural Artificial Buildings lawns grounds grounds Trees 552 73 0 0 0 Shrubs & lawns 6 665 0 0 0 Natural grounds 0 0 3684 1 283 Art. Grounds 0 0 0 1147 316 Buildings 0 0 344 1732 773 Total 558 738 4028 2880 1372 73/625 6/671 284/3968 316/1463 2076/2849 Commission 11.68 0.89 7.16 21.60 72.87 % 6/558 73/738 344/4028 1733/2880 599/1372 Omission 1.08 9.89 8.54 60.17 43.66 % Overall Accuracy = (6821/9576) ~ 71.2302% and Kappa Coefficient = 0.6048. Trees

Total 625 671 3968 1463 2849 9576

Table 3. Confusion matrix for classification with the LIDAR band Ground Truth Class (Pixels)

Trees Shrubs & lawns Natural grounds Art. grounds Buildings Total Commission % Omission %

Trees 552 6 0 0 0 558 32/584 5.48 6/558 1.08

Shrubs & lawns 32 706 0 0 0 738 6/712 0.84 32/738 4.34

Natural grounds 0 0 4028 0 0 4028 9/4037 0.22 0/4028 0.00

Artificial grounds 0 0 9 2871 0 2880 0/2871 0.00 9/2880 0.31

Buildings Total 0 0 0 0 1372 1372 0/1372 0.00 0/1372 0.00

584 712 4037 2871 1372 9576

Overall Accuracy = (9529/9576) ~ 99.5092% and Kappa Coefficient = 0.9930.

observed in Fig. 5; however, when LIDAR is included, the motorway is clearly detected except for the bridges that are taken as buildings, as can be seen in Fig. 6. To assess and compare the classification results numerically, confusion matrices were calculated for both classifications, without and with LIDAR. The confusion matrix for the classification of Fig. 5 can be seen in Table 2, while that corresponding to Fig. 6 is shown in Table 3.The overall accuracy and the Kappa coefficients are 71.2% and 0.604 (see Table 2) for the SVM classification without the LIDAR data, shown in Fig. 5, while the overall accuracy and the Kappa coefficient reach 99.5% and 0.993 respectively (see Table 3) for the SVM classification with the LIDAR data, shown in Fig. 6. Therefore, a gain of 28.3 % is obtained in merging the LIDAR data together with the multispectral bands for this data set. The confusion matrices allow us to examine the differences in the classifications in detail. For all the classes considered, the omission and commission errors are improved to some degree by the integration of LIDAR. For instance, in Table 2, the class of buildings has an omission error of 43.66% and a commission error of 72.87%, whereas when LIDAR is


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considered, these errors have disappeared (as can be seen in Table 3). This is a consequence of introducing elevation data, which allows building extraction to take place more accurately, as seen in the comparison between the classifications presented in Fig. 5 and Fig. 6. To a lesser degree than with the buildings, an improvement in the classification of vegetated areas can also be observed between high vegetation (trees) and low vegetation (lawns, grass or shrubs). However, better results were obtained for vegetation classes by Bork and Su [21] who combine LIDAR and multispectral imagery for the classification of vegetation, and they obtained an improvement of 16% and 20%, when considering three and eight classes, respectively. It is interesting to observe that the spectral signatures of some urban materials are similar in their composition and are assigned by the classifier to the same class; for instance, the roofs of some buildings are made of asphalt-based materials, and are assigned to artificial grounds because the material is similar to roads and parking lots. However, when LIDAR elevation data is considered, the classifier discriminates between buildings and artificial grounds, and these errors disappear.

6 Conclusions The effect of airborne LIDAR elevation data on the classification of multispectral SPOT5 imagery using SVM over a semi-urban area has been studied. After a numerical evaluation with confusion matrices including ground truth samples that are different from the ground truths used in the training phase, a gain of 28.3 % in overall accuracy has been obtained for the imagery used in this experiment. The classification results, with the SVM classifier combining LIDAR elevation data with multispectral SPOT5, show a more realistic and homogeneous distribution of geographic features than was obtained in using multispectral SPOT5 alone. The fact that the classification with LIDAR provides a clear definition of cartographic entities will allow the subsequent vectorization of the classification maps, and the incorporation of the vector objects into a geographical information system. Acknowledgments. The authors wish to thank the Spanish National Mapping Agency (Instituto Geográfico Nacional) for providing the images and LIDAR data for this research. The authors are also grateful to the Spanish MCYT for financial support to present this paper in ISVC08; project number CGL2006-07132/BTE.

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