GABOR AND WEBER LOCAL DESCRIPTORS PERFORMANCE IN MULTISPECTRAL EARTH OBSERVATION IMAGE DATA ANALYSIS

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1 HENRI COANDA AIR FORCE ACADEMY ROMANIA INTERNATIONAL CONFERENCE of SCIENTIFIC PAPER AFASES 015 Brasov, 8-30 May 015 GENERAL M.R. STEFANIK ARMED FORCES ACADEMY SLOVAK REPUBLIC GABOR AND WEBER LOCAL DESCRIPTORS PERFORMANCE IN MULTISPECTRAL EARTH OBSERVATION IMAGE DATA ANALYSIS Florin-Andrei Georgesu*, Mihai Datu**, Dan Răduanu*** *Military Tehnial Aademy, Buharest, Romania & Politehnia University of Buharest, Romania, **Politehnia University of Buharest, Romania & German Aerospae Center (DLR), Oberpfaffenhofen, Germany, ***Military Tehnial Aademy, Buharest, Romania Abstrat: During the last deades, both the satellite sensors and remote sensing imagery are evolving so fast that the methods and tehniques of proessing and analyzing Earth Observation (EO) data usually are staying one step behind. In the urrent paper, the authors goal was to prove the usability of Gabor and Weber Loal Desriptors (WLD) in multispetral image lassifiation. The extrated Gabor and WLD features were tested with Support Vetor Mahines (SVM), k-nearest Neighbors (k-nn) and k- Means lassifiers. Keywords: benhmarking, remote sensing, lassifiation, Gabor filtering, Webber Loal Desriptors, feature extration 1. INTRODUCTION In the field of remote sensing and EO image data proessing it is important to have at disposal a large variety of tools that an extrat the maximum relevant information from an image []. The appliability of remote sensing image lassifiation goes beyond the walls of the laboratory environment and an be used with suess in rises and disaster management, in loal administration and even in military appliations. Also the multispetral images provide us relevant information about the land over and land use. Until now, there is no general rule that an be applied to reate a universal information retrieval proedure regardless of the data being analyzed [3]. In most of the ases we must use speifi algorithms for speifi types of data. Most of the times, the image indexing methods are based on identifiation and lassifiation of image texture, image intensity or by using statistial models. The results are then grouped in a few generi lasses (3-6) like rops, buildings, streets, vegetation, forest et. [6] In this paper, the authors are presenting a benhmark of extrated Gabor and WLD image desriptors when using SVM, k-nn and k-means lassifiers. This benhmark idea is to gather relevant results on the performane of lassifiers when dealing with Gabor and WLD image desriptors in the ontext of multispetral image analyses. The goal is to determine the best lassifier that fits best the image ontent given the analyzed features.

2 . FEATURE SPACE AND IMAGE DESCRIPTORS Usually, in order to proeed with image lassifiation there are a few steps that must be taken into aount. Some of these steps refer to image pre-proessing, image feature extration and image lassifiation. The image pre-proessing step require that the analyzed image to be geometrially and radiometri orret. Also in the image pre-proessing step, the multispetral data is filtered and then is ut into image pathes of onveyable size. These pathes are used in the image feature extration step where the mean and standard deviation of eah path is omputed. After obtaining all the statistial image desriptors for eah path we are ready for the image lassifiation..1 Gabor features. It is known that texture lassifiation plays an important role in omputer vision and its appliations. Among various feature extration methods, filter bank method suh as Gabor filters has emerged as one of the most popular one. This filter bank is defined by its parameters inluding frequenies, orientations, frequeny ratio and smooth parameters of Gaussian envelope. [5] Figure 1. Gabor Filter example in spatial domain, with 4 orientations θ= 0 o, 45 o, 90 o and 135 o and sale parameters σ x = σ y = 3 pixels. Considering the texture harateristis and other related studies, one an onlude that the human visual system responds to texture properties suh as repetition, diretionality and omplexity [4], so the D Gabor filter an be expressed as in equation (1) using λ, θ,, σ, γ parameters that represent wavelength, orientation angle (in radians), phase offset, standard deviation and filter sale. In our study we made use of Gabor filter banks with 4 orientations and 3 sales that we apply on eah band of the extrated path. ( x' y' ) g (, ) os( ',,,, x y e x ) (1) b 1 ln 1 b 1 ln b log ln ' xos y sin y' xsin y os () (3) x (4). Weber Loal Desriptors (WLD). The Weber s law (Ernst Weber) states that the hange of a stimulus that will be just notieable is a onstant ratio of the original stimulus. [1] If the hange is smaller than this onstant ratio, it annot be reognized. In equation (5), ΔI represents the barely pereptible differene of two stimulus, I represents the initial intensity of the stimulus and the k meaning is that the ratio stays onstant regardless of I variations. This equation is known as Weber ratio. [4] (5) I k I p1 i0 p1 v ( x ) ( x x ) (6) s i i0 01 G ( x ), ratio i (7) p 1 ( xi x ) ( x ) artan artan (8) 01 i0 x 11 v s x artan (9) x x and x x (10) v s 5 1 v s The equations (8) and (9) are used when we speak about multispetral data. In the ase of Syntheti Aperture Radar (SAR) data, the situation is slightly different and an adapted WLD must be used [1]. 3. REMOTE SENSING IMAGE CLASSIFICATION Remote sensing image lassifiation is a ontinuous expanding domain and makes use of the multimedia image lassifiation tehniques that are modified and adapted to handle EO data. As it is well known, the proedures employed an be unsupervised (where no user effort is required), supervised (the user must prepare a training set) and 7 3

3 objet-based (based on multi resolution segmentation). In the present study we hosen a path based approah for image ontent lassifiation and we used supervised SVM and k-nn and unsupervised k-means on Gabor and WLD image feature desriptors. SVM represents a set of supervised learning methods used in automati lassifiation. The inputs of the algorithm are the training sets (stored into a database) and a test set (the omputed pathes). Also, the same priniple is used in the k-nn lassifiation, despite the of unsupervised k-means ase, were the user only speifies the number of lasses he desire to obtain. 4. EXPERIMENTAL RESULTS During the experimental stage, we used supervised SVM and k-nn lassifiation and unsupervised k-means lassifiation along with Gabor and WLD loal desriptors. In the frame of the experimental setup we are using a WorldView multispetral image that illustrates the area of Buharest, Romania (Figure 3). The sene overs almost 5 square kilometers and is haraterized by m spatial resolution, 8 spetral bands and 8 bits radiometri resolution representation. In our experiment, the image was resampled using nearest neighbors method in order to enhane the spatial resolution from m to 1 m. In the image lassifiation proess we used onveyable pathes of 50 pixels size that overs.5 square kilometers. During testing we onsidered 5 semanti ategories, as shown in Figure and Figure 7. In Figure 7 is the representation of manual annotation map used in the qualitative evaluation of the study. total number of pathes. In the ase of the unsupervised lassifiation no supplementary operations were needed. Figure 3. WorldView image, Buharest, Romania, 8 spetral bands, 8 bit radiometri resolution Figure 4. SVM Classifiation Figure 5. k-nn Classifiation Figure. Land over semanti ategories and assoiated olor representation used in lassifiation. In the ase of supervised lassifiation we used 0 samples per lass, meaning a total of 1 samples, from the input image. The seleted samples represent 0.01 from the Figure 6. k-means Classifiation

4 SVM k-nn k-means Cla Preisi Re Preisi Rea Preisi Rea ss on all on ll on ll C C C C C Table 1. Confusion matrix for Gabor features lassifiation with SVM, k-nn and k-means C1 C C3 C4 C5 SVM k NN k Means Figure 8. F-measure for Gabor features lassifiation SVM k-nn k-means Cla Preisi Re Preisi Rea Preisi Rea ss on all on ll on ll C C C C C Table. Confusion matrix for WLD features lassifiation with SVM, k-nn and k-means Figure 7. Referene annotation map In Figure 4, Figure 5 and Figure 6 are shown the results of the lassifiation benhmarking, as well in the Table 1 and Table we an see the onfusion matrixes resulted from the lassifiations. Figure 9. F-measure for WLD features lassifiation 5. CONCLUSIONS & ACKNOWLEDGMENT This paper has the purpose to demonstrate the usability of loal desriptors in the ase of multispetral EO image lassifiation. As it an be seen from the onfusion matrixes, the orret lassifiation sores are very high in the ase of lassifiation with SVM and k-nn and not so aurate when using unsupervised k-means. The reason we used in our tests not only supervised but also unsupervised lassifiation methods is that we wanted a method to validate the lass separation performane. As it an be seen from the F-measure harts, Figure 8 and Figure 9, in the multispetral image lassifiation is important not only the feature extration method but also the lassifiation method we use. The best results are obtained using SVM and k-nn supervised lassifiation for Gabor and WLD also. This paper has been finanially supported within the projet entitled Horizon 00 - Dotoral and Postdotoral Studies: Promoting the National Interest through Exellene, Competitiveness and responsibility in the Field of Romanian Fundamental and Applied Sientifi Researh, ontrat number POSDRU/159/1.5/S/ This projet is o-finaned by European Soial Fund through

5 Setorial Operational Programme for Human Resoures Development Investing in people! REFERENCES 1. Cui, S., Dumitru, C. O., Datu, M., Very High Resolution SAR Image Indexing Based on Ratio Operator, IEEE, GRS Letter, February 9, 01.. Cui, S., Datu, M., Casade Ative Learning for Evolution Pattern Extration From SAR Image Time Series, Multi Temp Dumitru, C.O., Datu, M., Information Content of Very High Resolution SAR Images: Study of Dependeny of SAR Image Struture with Inidene Angle, International Journal on Advanes in Teleomuniations, vol 5 no 3 & 4, Jie, C., Shiguang, S., Chu, H., Guoying, Z. Matti, P., Xilin, C., Wen, G., WLD: A robust Loal Image Desriptor, IEEE Transations on Pattern Analysis and mahine intelligene, 9 5. Pakdel, M., Tajeripour, F., Texture Classifiation Using Optimal Gabor Filters, 1st International econferene on Computer and Knowledge Engineering (ICCKE), Otober 13-14, Popesu, A.A., Gavat, I., Datu, M., Contextual Desriptors for Sene Classes in Very High Resolution SAR Images, IEEE Geosiene and Remote Sensing Letters, Vol. 9, No.1, January 01