DEVELOPING AN APPROACH TO DETECT URBAN EXPANSION BY USING LANDSAT 7 ETM+ PANCHROMATIC DATA Qingxu Huang, *Chunyang He, Peijun Shi, Yuanyuan Zhao, Yang Yang State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China. Corresponding Author˖
[email protected];
[email protected] Remotely sensed data play an important role in detecting urban expansion timely and effectively [1]. At present, a lot of sensors in satellite platforms can provide the panchromatic data and multispectrual data simultaneously, such as SPOT, IKONOS and Quickbird. Compared to the multispectral data obtained by the same sensor, the panchromatic data often have lower spectral resolution, but higher spatial resolution and more spatial information, such as structural and textural information. It has been proved the spatial detail is an important factor in improving the accuracy of classification and change detection [3~5], especially in the urban areas with obviously heterogeneous landscape due to its diverse components of materials: concrete, asphalt, metal, glass, water, grass and so on) [2, 6, 7]. In addition to multispectral bands, The Landsat 7 ETM+ also have a panchromatic band (Band 8) covering the visible green to near-IR (0.50~0.92ȝm) portion of the electromagnetic spectrum with the spatial resolution of 15m. Nowadays, applications of Landsat 7 ETM+ panchromatic data are restricted to fusion with multispectral data, generation of DEM and volcanic mapping [8, 9]. Less attention has been paid on urban expansion detection by using the multi-date Landsat 7 ETM+ panchromatic data directly with the support of its rich textural and structural information. Therefore, our paper tries to develop a new approach to detect urban expansion by using the multi-date Landsat 7 ETM+ panchromatic data directly. In this approach, the structural information and textural information extracted from the ETM+ panchromatic band were combined with the panchromatic itself to produce the multi-band panchromatic data. With the produced multi-band panchromatic data at different period, the urban changes were detected by using the popular change detection approach of change vector analysis (CVA). The district of Haidian, Beijing, China was used as the study area, which experienced fast urban expansion in last two decades due to economic development and population increase. Two high-quality Landsat ETM+ Panchromatic images acquired on 30th April, 2000 and 25th May, 2003 respectively and covered the whole study area were used to test the approach. The approach was implemented as follows: Step one: retrieving textural and structural information from Landsat 7 ETM+ panchromatic data Textural information is retrieved from the ETM+ panchromatic data depending on widely used Grey Level Co-occurrence Matrix (GLCM) [4]. Eight textural features are chosen from the total 14 features, including mean, variance, homogeneity, contrast, dissimilarity, entropy, angular second moment and correlation. Then the best moving window size for texture separation is decided by calculating the separability under different window sizes ranging from 3x3 to 11x11 [6]. Structural information, the edge density, was produced through the procedure which was proposed by Gong and Howarth [5]. Firstly, the ETM+ image was filtered using a Laplacian high pass filter; secondly, edges were found by thresholding the filtered image based on histogram interpretation. Then, the binary edge map was coded as “255” (marginal pixel) and “0” (nonmarginal pixel). Finally, an average filter was used to produce the edge density map. Step two: calculating change intensity based on standardized change vector analysis method Change vector analysis (CVA) is a robust change detection technique, which is valuable and flexible for analyzing multi layers of data [9]. Thereby, it has its advantage to combine the change of spectrum, texture and structure together. Change intensity map was computed by Euclidean distance between two temporal pixels. In order to guarantee each source (spectrum, texture, and structure) has the same contribution and can be compared equally; each source of data was standardized in computing the Euclidean distance in advance. Step three: the change/no change threshold was determined by support vector machine
Support vector machine (SVM) is a statistical learning theory introduced by Vapnik and his colleagues, which is increased used in real-life statistical [10]. In the field of remote sensing, SVM-based methods were used for feature selection and classification [11]. The typical change/no change training samples were selected to input to SVM software, libsvm 2.6. Then the separability was tested under different parameters (C, Ȗ) and kernel functions (linear kernel, polynomial kernel, radial basis function kernel and S-shape function kernel) [12]. Finally, the best change/no change classification map was obtained. Step four: accuracy assessment Another urban expansion map is produced only from the ETM+ panchromatic spectral data, based on the change detection procedure. Assessment of the two urban expansion maps was carried in a group of samples including typical change/no change areas, which was produced by auxiliary land use maps in Haidian District. The results suggest that the textural and structural information incorporated into the spectral information would be helpful for improving the accuracy of change detection. Now that ETM+ data are widely used for change detection, the new approach will be a useful attempt to fully understand and discover the potential information in this type of images. ACKNOWLEDGEMENT The research is supported by the natural scientific foundation of China (Grant No. 40501001) and national basic research program of China (Grant No. 2006CB400505). REFERENCES [1] Jensen J.R., Introductory Digital Image Processing:A Remote Sensing Perspective, 34d Ed, Prentice-Hall, Upper Saddle River, NJ, 2004. [2] A. Puissant, J. Hirsch and C. Weber, “The Utility of Texture Analysis to Improve per-pixel Classification for High to Very High Spatial Resolution Imagery,” International Journal of Remote Sensing, Vol. 26, No. 4, , pp. 733-745, 2005. [3] J.B.K. Kiema, “Texture Tone Analysis and Data Fusion in the Extraction of Topographic Objects from Satellite Imagery,” International Journal of Remote Sensing, Vol. 23, pp. 767-776, 2002. [4] R.M. Haralick, “Statistical and Structural Approaches to Texture,” Proceedings of the IEEE, Vol. 67: 786-804, 1973. [5] P. Gong, and P.J. Howarth, “The Use of Structural Information for Improving Land-Cover Classification Accuracies at the Rural-Urban Fringe,” PE&RS, 56(1): 67-73, 1990. [6] Q. Zhang, J. Wang, P. Gong, et al., “Study of Urban Spatial Patterns from SPOT Panchromatic Imagery Using Textural Analysis,” International Journal of Remote Sensing, 24(21): 4137-4160, 2003. [7] P.F. Luke, J. L. Andrew, and W. Robert, “Improved Identification of Volcanic Features Using Landsat 7 ETM+,” Remote Sensing of Environment, 78: 180-193, 2001. [8] J.G. Liu, “Evaluation of Landsat7 ETM+ Panchromatic Band for Image Fusion with Multispectral Bands,” Natural Resources Research, 9(4): 269-276, 2000. [9] R.D. Johnson, and E.S Kasischke, “Change Vector Analysis: A Technique for the Multispectral Monitoring of Land Cover and Condition,” International Journal of Remote Sensing, 19(3): 411-426, 1998. [10] Vapnik V.N., The Nature of Statistical Learning Theory, Springer Verlag, New York, 1995. [11] G. Zhu, D.G. Blumberg, “Classification Using ASTER Data and SVM Algorithm: The Case Study of Beer Sheva, Israel,” Remote Sensing of Environment, 80: 233-240, 2002. [12] Hsu C., Chang C., and Lin C., A Practical Guide to Support Vector Classification, http://www.csie.ntu.edu.tw/~cjlin, 2007.
