پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,525 |
| تعداد مقالات | 70,442 |
| تعداد مشاهده مقاله | 123,990,766 |
| تعداد دریافت فایل اصل مقاله | 97,111,856 |
کارایی شاخص‌های راداری در استخراج سطوح نفوذناپذیر شهری با استفاده از تصویر رادار تمام پلاریمتریک | ||
| پژوهش‌های جغرافیای برنامه‌ریزی شهری | ||
| مقاله 10، دوره 7، شماره 4، دی 1398، صفحه 837-854 اصل مقاله (1.15 M) | ||
| نوع مقاله: پژوهشی - کاربردی | ||
| شناسه دیجیتال (DOI): 10.22059/jurbangeo.2019.288561.1162 | ||
| نویسنده | ||
| سارا عطارچی* | ||
| استادیار گروه سنجش‌ازدور و سیستم اطلاعات جغرافیایی، دانشکدة جغرافیا، دانشگاه تهران، تهران، ایران | ||
| چکیده | ||
| تفکیک سطوح نفوذناپذیر در مناطق شهری و بررسی روند تغییرات آن، اهمیت بسیاری دارد؛ زیرا امروزه این مقوله شاخصی از گسترش شهر به‌شمار می‌آید. سطوح نفوذناپذیر در مناطق شهری، شامل مناطق مسکونی، مناطق تجاری و صنعتی، پارکینگ‌ها و سطح معابر و شبکة خیابان‌هاست. انواع سطوح نفوذناپذیر و تنوع بسیار آن‌ها از نظر شکل، اندازه و مواد تشکیل‌دهنده سبب پیچیدگی تفکیک این سطوح در مناطق شهری می‌شود. در این پژوهش از تصویر سار تمام پولاریمتریک سنجندة آلوس/ پالسار برای تشخیص سطوح نفوذناپذیر در سطح شهر تهران استفاده شده است. کارایی شاخص‌های راداری مختلف و ترکیب دوگانة باندهای پولاریمتریک در تفکیک سطوح نفوذناپذیر از سایر کلاس‌های متفاوت پوشش زمین، ارزیابی شده است. برای شناسایی کلاس‌های پوشش زمین از الگوریتم طبقه‌بندی ماشین بردار پشتیبان استفاده شده است. براساس نتایج پژوهش، استفاده از شاخص‌های راداری همراه با تمام باندهای پولاریمتریک، سبب استخراج انواع سطوح نفوذناپذیر با صحت 95 درصد می‌شود. همچنین در صورت استفاده از دو باند پولاریمتریک با قطب عمودی‌اش به همراه شاخص‌های راداری، صحت طبقه‌بندی 90 درصد است. براساس یافته‌های این پژوهش، تصاویر سار جایگزین مناسبی برای تصاویر نوری در تفکیک سطوح نفوذناپذیر شهری هستند. همچنین در صورت دسترسی‌نداشتن به تصاویر تمام پولاریمتریک، استفاده از دو باند پولاریمتریک به همراه شاخص‌های راداری، برای استخراج سطوح نفوذناپذیر در مناطق پیچیده شهری مناسب است. | ||
| کلیدواژه‌ها | ||
| آلوس/ پالسار؛ سار تمام پولاریمتریک؛ سطوح نفوذناپذیر شهری؛ شاخص‌های راداری؛ طبقه‌بندی ماشین بردار پشتیبان | ||
| عنوان مقاله [English] | ||
| Efficiency Evaluation of SAR-derived Indices in Urban Impervious Surfaces Extraction using Full Polarimetric Image | ||
| نویسندگان [English] | ||
| Sara Attarchi | ||
| Assistant professor of Remote Sensing, Remote Sensing and GIS Department, Faculty of Geography, University of Tehran | ||
| چکیده [English] | ||
| Introduction Impervious surfaces are the surfaces on which water cannot infiltrate. Detection of urban impervious surfaces is of great importance because the extension of these surfaces is an indicator of built-up area expansion and population growth. In recent years, remote sensing images have been widely used for land cover /land use studies. The efficiency of optical images has been widely explored in impervious surface delineation in urban areas. However, detection of impervious surface is not a simple task. Impervious surfaces vary in size, shape and material. Similar spectral responses among impervious surfaces and other types of land cover make the separation of impervious surfaces and other classes challenging. Synthetic Aperture Radar (SAR) images are getting more and more attention in urban areas mapping. However, most of the studies concentrated on fusion of optical and SAR images or single polarized data. Full polarimetric SAR images offer more capabilities in separation of different land cover classes because, in full polarimetric mode, all characteristics of object’s backscattering will be perceived. SAR indices are computed based on data of two or more polarimetric bands. Therefore, they contain more information of land cover classes. Although the calculation and interpretation of SAR indices is simple, they are not fully understood in impervious surface detection in an urban environment. For impervious surface extraction, different classifiers have been used such as maximum likelihood, support vector machine and neural network. Among them, non-parametric classifiers often reach higher classification accuracies. Therefore, in this study support vector machine (SVM) algorithm has been applied. Since the efficiency of full polarimetric SAR has not been evaluated for urban impervious surfaces, this study focused on the extraction of these surfaces in the complex urban area by the L-band full polarimetric SAR image. Most of the previous studies focused on optical images as well as the fusion of optical and SAR images. In cloudy and rainy weather, optical images are not available. In such a situation, the use of optical image and fusion of optical and SAR images are not possible. Therefore, we have studied the independent use of SAR images and extracted SAR indices. Methodology Tehran has been chosen as the study area since it has a complex structure. Tehran is the capital of Iran and is accounted as the economic and commercial center of Iran. Different impervious surfaces are found in this city. Impervious surfaces include residential areas, commercial areas, highways and parking lots. These surfaces are very diverse in terms of size and materials. As an example, narrow streets, irregular streets as well as wide and regular wide highways all exist in this city. Impervious surfaces may be perceived as dark and bright impervious surfaces in SAR images. Residential area and the area covered by new cement have high backscattering values and appear bright in SAR image. Streets and old cement have lower backscattering values and appear darker in this image. In addition to the impervious surface, vegetation, water body and bare land are also found in Tehran. One Advanced Land Observing Satellite / Phased Array type L-band Synthetic Aperture Radar (ALOS/PALSAR) scene acquired on 23 April 2009 which encompassed Tehran has been selected. This scene has four polarimetric bands; HH, HV, VH and VV. The image has been processed and DN values have been converted to sigma nought in decibel. In order to reduce the topographic effect, radiometric terrain correction has been applied. Enhance Lee filter has been applied to minimize the speckle effect. Ratio index, average index, difference index, normalized difference index and NLI index have been computed by the different combination of two polarimetric bands. It is proved that SAR indices are effective in separating different land cover classes. For classification purpose, support vector machine algorithm has been applied. SVM is a non-parametric classifier that has been applied extensively in SAR applications. It has no previous assumption of the statistical distribution of data. Training samples have been chosen on high spatial and historical google earth image. Approximately 500 pixels have been selected for each land cover class. The classification was followed by five scenarios; in the first scenario, only four polarimetric bands were used. In the second scenario, bands HH-HV and driven indices were classified. In the third scenario, bands HV-VV and their SAR indices were considered. Co-polarized bands (HH and VV) and SAR indices calculated based on these two bands were interred in the fourth scenario. Four polarimetric bands and all extracted indices were inserted in the fifth scenario. For accuracy assessment, circa 300 pixels were selected independently for each class as validation samples on high spatial and historical google earth image. By comparing classification results with validation samples confusion matrices were constructed. Based on the confusion matrix, overall accuracy, Kappa coefficient, producer and user accuracies were computed. Results and discussion The overall classification accuracy of the first scenario was 92.67% and the kappa coefficient was 0.9. This shows full polarimetric SAR images are capable of delineating impervious surfaces in the complex urban area. Band combination of HH-HV and driven indices yield overall accuracy and kappa coefficient, 84.23% and 0.78, respectively. The third scenario reached to 90.30% for classification accuracy and 0.86 for the kappa coefficient. The highest classification accuracy from two polarimetric bands is achieved by this scenario. These results could be justified by the presence of vertical polarization in both bands. Diverse vertical structures in the urban texture could be better distinguished by vertical polarization. The combination of co-polarized bands and their indices has 79.16% classification accuracy and 0.7 kappa coefficient. The lowest accuracy belongs to this scenario. The absence of cross-polarized bands may cause such relatively poor results. Depolarization is dominant in a well-developed urban area and co-polarized bands are not capable to capture depolarization. The last scenario reached the highest classification accuracy; 95.59% for overall accuracy and 0.96 for the kappa coefficient. The comparison between the first and last scenario shows the importance of SAR indices. Conclusion Three main conclusions can be driven from the findings of this study. First, full polarimetric bands are capable of urban impervious surface extraction. This is of great importance, especially in the absence of optical images. Second, dual polarimetric SAR images and their driven indices can extract impervious surface efficiently. Since most of SAR sensors work in dual mode, dual polarimetric SAR images have high availability. This study shows by the help of SAR indices, dual polarimetric can be used alternatively. And the last conclusion implies the importance of vertical polarization. In case, vertical polarization exists in both polarimetric bands, high classification accuracy will be achieved. | ||
| کلیدواژه‌ها [English] | ||
| Urban impervious surface, full polarimetric SAR, SAR indices, Support vector machine, ALOS/PALSAR | ||
| مراجع | ||
|
زائری امیرانی، آزاده و علیرضا سفیانیان، 1391، تهیه نقشه سطوح نفوذناپذیر به عنوان یک شاخص زیست محیطی، فصلنامه علمی-پژوهشی اطلاعات جغرافیایی سپهر، دوره 21، شماره 83، صص 65-59. ARNOLD JR, C. L. and GIBBONS, C. J., 1996, Impervious Surface Coverage: The Emergence of a Key Environmental Indicator, Journal of The American Planning Association, No. 62, PP. 243-258. BRABEC, E., SCHULTE, S.,and RICHARDS, P. L. 2002. Impervious Surfaces and Water Quality: A Review of Current Literature and Its Implications for Watershed Planning, Journal of Planning Literature, No. 16, PP. 499-514. DENG, C. and WU, C., 2013, Examining the Impacts of Urban Biophysical Compositions on Surface Urban Heat Island: A Spectral Unmixing and Thermal Mixing Approach, Remote Sensing of Environment, No. 131, PP. 262-274. Dong, J., Xiao, X., Sheldon, S., Biradar, C., Duong, N. D. And Hazarika, M. 2012, A Comparison of Forest Cover Maps in Mainland Southeast Asia From Multiple Sources: PALSAR, MERIS, MODIS and FRA, Remote Sensing of Environment, No. 127, PP. 60-73. Dong, J., Xiao, X., Sheldon, S., Biradar, C., Zhang, G., Duong, N. D., Hazarika, M., Wikantika, K., Takeuhci, W., and Moore Iii, B., 2014. A 50-M Forest Cover Map in Southeast Asia From Alos/Palsar And Its Application on Forest Fragmentation Assessment, Plos One, 9, E85801. Gamba, P., and Houshmand, B., 2001, An Efficient Neural Classification Chain of Sar and Optical Urban Images, International Journal of Remote Sensing, No. 22, PP. 1535-1553. GUO, H., YANG, H., SUN, Z., LI, X. and WANG, C., 2014, Synergistic Use of Optical and Polsar Imagery for Urban Impervious Surface Estimation, Photogrammetric Engineering and Remote Sensing, No. 80, PP. 91-102. HU, X., and WENG, Q., 2009, Estimating Impervious Surfaces From Medium Spatial Resolution Imagery Using the Self-Organizing Map and Multi-Layer Perceptron Neural Networks, Remote Sensing of Environment, No. 113, PP. 2089-2102. HU, X., and WENG, Q., 2011, Estimating Impervious Surfaces From Medium Spatial Resolution Imagery: A Comparison Between Fuzzy Classification And LSMA, International Journal of Remote Sensing, No. 32, PP. 5645-5663. Jensen, J. R., and Lulla, K., 1987, Introductory Digital Image Processing: A Remote Sensing Perspective. Jiang, L., Liao, M., Lin, H., and Yang, L., 2009, Synergistic Use of Optical and Insar Data for Urban Impervious Surface Mapping: A Case Study In Hong Kong, International Journal of Remote Sensing, No. 30, PP. 2781-2796. Lee, J. S., Jurkevich, L., Dewaele, P., Wambacq, P., and Oosterlinck, A., 1994, Speckle Filtering of Synthetic Aperture Radar Images: A Review, Remote Sensing Reviews, No. 8, PP. 313-340. Leinenkugel, P., Esch, T., ans Kuenzer, C., 2011, Settlement Detection and Impervious Surface Estimation in the Mekong Delta Using Optical and SAR Remote Sensing Data, Remote Sensing of Environment, No. 115, PP. 3007-3019. Li, G., Lu, D., Moran, E., Dutra, L., and Batistella, M., 2012, A Comparative Analysis of ALOS PALSAR L-Band and RADARSAT-2 C-Band Data for Land-Cover Classification in a Tropical Moist Region, ISPRS Journal of Photogrammetry and Remote Sensing, No. 70, PP. 26-38. Longepe, N., Rakwatin, P., Isoguchi, O., Shimada, M., Uryu, Y., and Yulianto, K., 2011, Assessment of Alos Palsar 50 M Orthorectified Fbd Data for Regional Land Cover Classification By Support Vector Machines, Ieee Transactions on Geoscience and Remote Sensing, No. 49, PP. 2135-2150. MA, Q., HE, C., WU, J., LIU, Z., ZHANG, Q., and SUN, Z., 2014, Quantifying Spatiotemporal Patterns of Urban Impervious Surfaces in China: An Improved Assessment Using Nighttime Light Data, Landscape and Urban Planning, No. 130, PP. 36-49. Mountrakis, G., Im, J., and Ogole, C., 2011, Support Vector Machines in Remote Sensing: A Review. ISPRS Journal of Photogrammetry and Remote Sensing, No. 66, PP. 247-259. Qin, Y., Xiao, X., Dong, J., Chen, B., Liu, F., Zhang, G., Zhang, Y., Wang, J., and Wu, X., 2017, Quantifying Annual Changes in Built-Up Area in Complex Urban-Rural Landscapes From Analyses of PALSAR and Landsat Images, ISPRS Journal of Photogrammetry and Remote Sensing, No. 124, PP. 89-105. Qin, Y., Xiao, X., Dong, J., Zhang, G., Shimada, M., Liu, J., Li, C., Kou, W., and Moore Iii, B. 2015, Forest Cover Maps of China In 2010 From Multiple Approaches and Data Sources: PALSAR, Landsat, MODIS, FRA, and NFI, ISPRS Journal of Photogrammetry and Remote Sensing, No. 109, PP. 1-16. Shimada, M., Isoguchi, O., Tadono, T., Higuchi, R., and Isono, K., Palsar Calval Summary and Update 2007, 2007 Ieee International Geoscience and Remote Sensing Symposium, 2007. Ieee, PP. 3593-3596. Statistical Center of Iran, I, 2016, Population and Housing Censuses [Online]. Available: https://www.amar.org.ir/english/population-and-housing-censuses [accessed 2019.07.01]. Walker, W. S., Stickler, C. M., Kellndorfer, J. M., Kirsch, K. M., and Nepstad, D. C., 2010, Large-Area Classification and Mapping of Forest and Land Cover in the Brazilian Amazon: A Comparative Analysis of ALOS/PALSAR and Landsat Data Sources, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, No. 3, PP. 594-604. Weng, Q., 2012, Remote Sensing of Impervious Surfaces in the Urban Areas: Requirements, Methods, and Trends, Remote Sensing of Environment, No. 117, PP. 34-49. Weng, Q., and HU, X., 2008, Medium Spatial Resolution Satellite Imagery for Estimating and Mapping Urban Impervious Surfaces Using LSMA and ANN, IEEE Transactions on Geoscience and Remote Sensing, No. 46, PP. 2397-2406. WU, J., and Thompson, J., 2013, Quantifying Impervious Surface Changes Using Time Series Planimetric Data From 1940 to 2011 in Four Central Iowa Cities, USA, Landscape and Urban Planning, No. 120, PP. 34-47. Yang, L., Huang, C., Homer, C. G., Wylie, B. K., and Coan, M. J., 2003, An Approach for Mapping Large-Area Impervious Surfaces: Synergistic Use of Landsat-7 ETM+ and High Spatial Resolution Imagery. Canadian Journal of Remote Sensing, No. 29, PP. 230-240. Yang, L., Jiang, L., Lin, H., and Liao, M., 2009, Quantifying Sub-Pixel Urban Impervious Surface Through Fusion of Optical and Insar Imagery, Giscience and Remote Sensing, Vol. 46, No. 2, PP.161-171. Zaeri Amirani, A., Sofyanian, A., 2012, Preparation of Infiltration Levels Mapping As An Environmental Indicator, Scientific- Research Quarterly of Geographical Data (SEPEHR), Vol. 21, No. 83, PP. 65-69. Zhang, H., Lin, H., and Wang, Y., 2018, A New Scheme for Urban Impervious Surface Classification From SAR Images, ISPRS Journal of Photogrammetry and Remote Sensing, No. 139, PP. 103-118. Zhang, H., Zhang, Y., and Lin, H., 2012, A Comparison Study of Impervious Surfaces Estimation Using Optical and SAR Remote Sensing Images, International Journal of Applied Earth Observation and Geoinformation, No. 18, PP. 148-156. Zhang, J., Pu, R., Yuan, L., Wang, J., Huang, W., and Yang, G., 2014a, Monitoring Powdery Mildew of Winter Wheat by Using Moderate Resolution Multi-Temporal Satellite Imagery, Plos One, 9, E93107. Zhang, Y., Zhang, H., and Lin, H., 2014b, Improving the Impervious Surface Estimation with Combined Use of Optical and SAR Remote Sensing Images, Remote Sensing of Environment, No. 141, PP. 155-167. | ||
|
آمار تعداد مشاهده مقاله: 512 تعداد دریافت فایل اصل مقاله: 1,105 |
||