Very High Resolution Satellite Images Filtering Assia Kourgli LTIR, Faculté d’Electronique et d’Informatique U.S.T.H.B., Bab-Ezzouar Alger, Algérie assiakourgli@gmail.com Youcef Oukil Département de Géographie E.N.S. de Bouzareah Alger, Algérie Y_oukil@yahoo.fr Abstract—This paper introduces a new non linear filter suitable for artifacts (trees, cars, etc.) removing in Very High Resolution Satellite (VHRS) Images. It is based on the application of three filters of different sizes and shapes that permits each time to remove the artifacts smaller than the filter. These filters are based on the research of noisy pixels surrounded by homogenous area according to the three filters. First, tests have been conducted on Berkeley database images corrupted with different kinds of noise comparing the filter performances to that of anisotropic diffusion and bilateral filters. In a second stage, we applied our filtering technique to HRS images obtaining promising results. Keywords—edge preserving smoothing filter; very high resolution satellite images. I. INTRODUCTION The very HSR (High Spatial Resolution) satellite data represent the surface of the Earth with more detail but this induces an increase of the internal variability within homogenous land cover units. In this context, image segmentation that permits to create regions instead of pixels was proposed as a pre-processing step [3], [4]. This is, then, used as input to a land-use classifier. In remote sensing, image segmentation is desired to provide meaningful object primitives for further feature recognition and thematic classification [7], [8], [10], [11]. But, until now, there is no universal segmentation technique. Most of existing techniques suffer from the presence of geometric noise inherent to this kind of images. Indeed, the very high-spatial resolution of the image and the urban context induce a significant increase in geometric noise and artifacts such as vehicles, road markings, trees, occlusions or shadows that will disrupt the process of automatic extraction. To reduce the artifacts and make segmentation results more homogenous, we propose an iterative process of local filtering, which minimizes the average heterogeneity of the generated images by reducing artifacts. The first tests were carried out on the basis of images extracted from Berkeley database. We applied to these textured images different types of noise (Gaussian, Speckle and Salt & Pepper noises). Once validation is complete, we tested our filter on a very high spatial resolution satellite image taken from Google Earth software. The paper is organized as follows: In the next section, we present the proposed filter. The tests are presented and discussed in Section 3. The last section presents filtering results obtained on very high resolution satellite images. We end with a conclusion and perspectives for this work. II. COLOR IMAGE FILTERING Fragmentation is a key problem that exists in HSR image segmentation. It can cause undesirable holes in regions. Moreover, artifacts or small pieces of information can decrease segmentation efficiency. These problems exist more predominately in some solutions than in others and can cause difficulties for successful classification [5]. We referred to some classical filters usually employed with color images for smoothing images whereas preserving contours. The most successful filters are non linear ones such as anisotropic diffusion [6], bilateral filter [9], mean-shift filter [2]. However, none of them satisfy this goal perfectly: they each have exception cases in which smoothing may occur across hard edges and consequently affect the following processing steps (segmentation, classification, features extraction, etc.). Moreover, these filters are less efficient when the images are strongly textured and the denoising process may destroy the image edge structures. Our filter tries to overcome these problems by a new way to formulate smooth filtering. Usually, smoothing methods which are edge preserving are based on the general idea that the average is computed only from those points in the neighborhood which have similar properties to the processed point including the color of the processed point in the estimation. Our filter is not based on the comparison of the pixel under consideration to its neighbors, but is built around the idea of researching homogenous regions surrounding noisy pixels (artifacts in VHR satellite images). Once the homogenous regions are identified, their mean color is affected to the set of pixels they are surrounding. To find the homogenous regions, we use the three following filters: 2013 Eighth International Conference on Broadband, Wireless Computing, Communication and Applications 978-0-7695-5093-0/13 $31.00 © 2013 IEEE DOI 10.1109/BWCCA.2013.81 465 2013 Eighth International Conference on Broadband, Wireless Computing, Communication and Applications 978-0-7695-5093-0/13 $31.00 © 2013 IEEE DOI 10.1109/BWCCA.2013.81 465