ANISOTROPIC ROTATION INVARIANT BUILT-UP PRESENCE INDEX: APPLICATIONS TO SAR DATA P. Gamba(2), M. Pesaresi(1), K. Molch(1), A. Gerhardinger(1), and G. Lisini(2) (1) European Commission - Joint Research Centre, Ispra, Italy (2) Department of Electronics, University of Pavia Via Ferrata, 1, 27100 Pavia, Italy Corresponding author:
[email protected]
Recent works in technical literature introduced different indexes for understanding the presence of built-up areas in spaceborne remote sensing imagery. All of them are based on spatial analysis of the data, considering for instance different texture [1,2] or statistical [3] measures. However, many of them have been checked only on data sets by a peculiar sensor or sensor type, and without validation on many different test sites and various imagery acquisition conditions. This work is dedicated to overcome some of these shortcomings by testing on SAR data a recently introduced index, the anisotropic rotation invariant built-up presence index, proposed in [4]. The index was designed for optical images, specifically to be robust to errors in data calibration, problems. The index is based on the calculation of texture measures derived from the gray-level cooccurrence matrix (GLCM) as introduced by [5]. Opposed to traditional approaches, a number of different displacement vectors are considered, thereby exploiting the anisotropic nature of the texture feature as possibly present in the image. The rotation-invariant texture measure is derived through an integration using the basic logic operators of or U resulting in a description of the textural parameter independent of its specific direcionality. Applying the index to SAR data is exploiting the fact that spatial relationships (and thus contrast measures) do not depend on the sensor type. Our approach, however, does take into account the fact that SAR data are acquired in slant-range and then reprojected to ground range, and this usually reduces the possibility to detect small settlements and/or man-made structures and targets. To this aim, a different version of the index in the slant rage domain has been developed. It basically redefines the window dimensions and evaluated directions for contrast computation in order to match the slant range projections and computes the index before the ground range reprojection. Because of the non-linear nature of the slant to ground range transformation, the two index definitions are not equivalent, and this work proposes a first comparison of the two index values in a very complex urban environment. To this aim, the sample shown in Fig. 1 refers to a RADARSAT-1 Fine Mode image of Nairobi (Kenya). It is easy to observe that there are differences between the two approaches, better visualized by means of the superposition of know built-up areas’ boundaries in the area. The slant-range index is generally more able to discriminate between built up areas and their surroundings. However, it provides more variable and somehow noisier results, less useful for instance to discriminate among urban environments with various amounts of
vegetation. Finally, both definitions provides values with strong correlation to the actual built-up areas, unless for the areas where the viewing angle of the SAR sensor makes it unable to receive high backscatter values from man-made targets because of the relative positions between these objects and the sensor. In this situation, combination of more SAR data is likely to reduce the problem [6].
Fig.1: Comparison of ground range (left) and slant-range (right) computed built-up presence index using a RADARSAT-1 of Nairobi (Kenya)
REFERENCES [1] A. Baraldi and F. Parmiggiani, “An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters”, IEEE Trans. on Geoscience and Remote Sensing, vol. 33, pp. 293–304, 1995. [2] F. Dell’Acqua, P. Gamba: “Texture-based characterization of urban environments on satellite SAR images”, IEEE Trans. on Geoscience and Remote Sensing, Vol. 41, n. 1, pp. 153-159, Jan. 2003. [3] He, C., G.-S. Xia, H. Sun, “An adaptive and iterative method of urban area extraction from SAR images”, IEEE Remote Sens. Lett., available on-line at http://ieeexplore.ieee.org/iel5/8859/28004/101109LGRS2006878447.pdf [4] M. Pesaresi, A. Gerhardinger. F. Kayitakire, “Monitoring settlement dynamics by anisotropic textural analysis by panchromatic VHR data”, in Proc. of JURSE 2007, Paris, 11-13 April 2007, unformatted CD-ROM. [5] R.M Harlick, K. Shanmugam, and I.Dinstein., 1973, Texture features for image classification. IEEE Trans. on Systems, Man and Cybernetics, 3, 610-621. [6] F. Dell’Acqua, P. Gamba, G. Lisini: “Improvements to urban area characterization using multitemporal and multiangle SAR images”, IEEE Trans. on Geoscience and Remote Sensing, Vol. 41, n. 9, pp. 1996-2004, Sept. 2003.
