طبقه‏بندی شیءگرای مناطق شهری با تلفیق تصاویر سنتینل‌ـ 1 و سنتینل‌ـ 2

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار دانشکدة مهندسی نقشه‌برداری و اطلاعات مکانی، دانشکدگان فنی دانشگاه تهران

2 کارشناس ارشد دانشکدة مهندسی نقشه‌برداری و اطلاعات مکانی، دانشکدگان فنی دانشگاه تهران

چکیده

نقشه‌های کاربری زمین، توزیع فضایی منابع طبیعی، مناظر فرهنگی و سکونتگاه‌های انسانی را توصیف می‌کنند که به‌منزلة ابزار برنامه‌ریزی مهمی برای تصمیم‌گیرندگان عمل می‌کند؛ بنابراین دقت نقشه‏های حاصل از طبقه‏بندی تصاویر ماهواره‏ای در عدم قطعیت به‌منظور ‏مدیریت شهری بسیار تأثیرگذار است. با توجه به کیفیت یکنواخت تصاویر در مناطق بزرگ در مراحل زمانی منظم، تصاویر سنجش از دور ورودی ضروری در تولید نقشه‌های کاربری زمین شمرده می‌شوند. هدف اصلی از این تحقیق پیشنهاد‌کردن روشی به‌منظور ایجاد نقشة پوشش اراضی دقیق در مناطق شهری، با استفاده از تلفیق داده‏های سنتینل‌ـ 1 و سنتینل‌ـ 2 است. به این منظور، ویژگی‌های ضریب بازپراکنش راداری VV و دو پارامتر حاصل از روش تجزیة H-α (آنتروپی، آلفا) از تصاویر راداری سنتینل‌ـ 1 و ویژگی‌های باند آبی، سبز، قرمز، شاخص‌های NDVI، NDWI، MNDWI و SWI از تصاویر اپتیک سنتینل‌ـ 2 استخراج و به منزلة مؤلفه‌های تأثیرگذار در طبقه‌بندی منطقة شهری استفاده شد. در این مطالعه، با هدف جداسازی مناطق کشاورزی از سایر پوشش‌ها، از شاخص رطوبت SWI استفاده شد. همچنین داده‌های ارتفاعی برای تفکیک بهینة کلاس‌های پیچیده با توپوگرافی متفاوت به‌کار رفت. استخراج شاخص‌های تأثیرگذار از این دو مجموعه داده، در رویکردی شیءگرا مبتنی بر الگوریتم‌­های ماشین بردار پشتیبان و جنگل تصادفی، برای طبقه‌بندی کاربری زمین ارزیابی شد. نتایج نشان داد که به‌کارگرفتن ویژگی‌های استخراج‌شده از تصاویر راداری و اپتیک به‌طور هم‌زمان، در روش‌ طبقه‌بندی شیء‌گرا، می‏تواند ویژگی‏های شیء را به‌طور کامل در ناحیة مورد مطالعه استخراج کند. در مورد هر دو الگوریتم طبقه‏بندی، زمانی که از داده‏های اپتیک و راداری به‌طور هم‌زمان استفاده شد، دقت کلی طبقه‌بندی افزایش داشت. در مورد روش جنگل تصادفی که بیشترین دقت‌ها را دربر داشت، دقت کلی برای رویکرد ترکیب داده‌های راداری و اپتیک به‌میزان 13 و 5%، به‌ترتیب به نسبت رویکرد تنها ویژگی راداری و تنها رویکرد ویژگی اپتیک، افزایش پیدا کرده است. همچنین اختلاف معنی‏داری در دقت طبقه‏بندی، در تمامی سطوح، بین الگوریتم طبقه‌بندی ماشین بردار پشتیبان و جنگل تصادفی دیده می‌شود. نتایج نشان داد که دقت کلی درمورد روش طبقه‌بندی جنگل تصادفی و ماشین بردار پشتیبان به‌ترتیب برابر 3/83 و 8/79% و ضریب کاپا به‌ترتیب 72/0 و 68/0% بوده است.

کلیدواژه‌ها

عنوان مقاله [English]

Object-Oriented Classification of Urban Areas Using a Combination of Sentinel-1 and Sentinel-2 Images

نویسندگان [English]

  • Reza Shahhoseini 1
  • Kamal Azizi 2
  • Arastou Zarei 2
  • Fatemeh Moradi 2
1 Assistant Prof., School of Surveying & Geospatial Engineering, College of Engineering, University of Tehran
2 M.Sc. Student, School of Surveying & Geospatial Engineering, College of Engineering, University of Tehran
چکیده [English]

Land use maps describe the spatial distribution of natural resources, cultural landscapes, and human settlements that are essential for decision-makers. Therefore, the accuracy of maps obtained from the classification of satellite images is very effective in uncertainty for urban management. Due to the uniform quality of images in large areas at regular intervals, remote sensing images are essential for land use maps. The primary purpose of this study is to present a proposed method to create an accurate land cover map in urban areas using a combination of Sentinel-1 and Sentinel-2 data. For this purpose, the features of the backscattering coefficient VV and the two parameters obtained from the H-α decomposition method (entropy, alpha) of Sentinel-1 radar images and the features of the blue, green, red band, NDVI, NDWI, MNDWI, and SWI were extracted from Sentinel-2 Multispectral images and used as influential components to classify the urban area. To separate agricultural areas from other coatings, the SWI index was used. Elevation data have also been used to optimally distinguish complex classes with different topographies. We evaluated the extraction of effective indicators from these two datasets in an object-oriented approach based on support vector machine algorithms and random forest for land use classification. The results showed that using properties extracted from radar and Multispectral images simultaneously in the object-oriented classification method could altogether determinate the object's properties in the study area. When optical and radar data were used simultaneously for both classification algorithms, the overall accuracy classification increased. For the stochastic forest method, which provided the highest accuracy, the overall accuracy for the radar and optics data combination approach increased by 13% and 5%, respectively, compared to the radar feature approach and the optics feature approach alone. There was also a significant difference in classification accuracy at all levels between the support vector machine classification algorithm and the random forest. The results showed that the random forest classification method's overall accuracy and support vector machines were 83.3 and 79.8%, respectively, and the kappa coefficient was 0.72 and 0.68%, respectively.
 

کلیدواژه‌ها [English]

  • Remote sensing
  • Sentinel-1
  • Sentinel-2
  • Object-oriented classification
  • Support vector machine
  • Random forest

مراجع

Amarsaikhan, D., Blotevogel, H.H., Van Genderen, J.L., Ganzorig, M., Gantuya, R. & Nergui, B., 2010, Fusing High-Resolution SAR and Optical Imagery for Improved Urban Land Cover Study and Classification, International Journal of Image and Data Fusion, 1(1), PP. 83-97.
Bassa, Z., Bob, U., Szantoi, Z. & Ismail, R., 2016, Land Cover and Land Use Mapping of the isimangaliso Wetland Park, South Africa: Comparison of Oblique and Orthogonal Random Forest Algorithms, Journal of Applied Remote Sensing, 1-22, P. 22.
Boehner, J., Koethe, R., Conrad, O., Gross, J., Ringeler, A. & Selige, T., 2002, Soil Regionalisation by Means of Terrain Analysis and Process Parameterisation, In: Micheli, E., Nachtergaele, F., Montanarella, L. [Ed.]: Soil Classification 2001, European Soil Bureau, Research Report No. 7, EUR 20398 EN, Luxembourg, PP. 213-222.
Breiman, L., 2001, Random Forests, Machine Learning, 45(1), PP. 5-32.
Chen, M., Su, W., Li, L., Zhang, C., Yue, A. & Li, H., 2009, Comparison of Pixel-Based and Object-Oriented Knowledge-Based Classification Methods Using SPOT5 Imagery, WSEAS Transactions on Information Science and Applications, 6(3), PP. 477-489.
Cloude, S.R. & Pottier, E., 1997, An Entropy Based Classification Scheme for Land Applications of Polarimetric SAR, IEEE Transactions on Geoscience and Remote Sensing, 35(1), PP. 68-78.
Congalton, R.G., 1991, A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data, Remote Sensing of Environment, 37(1), PP. 35-46.
Dadras Javan, F., Mortazavi, F.S., Moradi, F. & Toosi, A., 2019, New Hybrid Pan-Sharpening Method Based on Type-1 Fuzzy-Dwt Strategy, International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences.
Darwish, A., Leukert, K. & Reinhardt, W., 2003, Image Segmentation for the Purpose of Object-Based Classification, In IGARSS 2003, 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No. 03CH37477) (Vol. 3, PP. 2039-2041), IEEE.
de Almeida Furtado, L.F., Freire Silva, T.S. & de Moraes Novo, E.M.L., 2016, Dual-Season and Full-Polarimetric C Band SAR Assessment for Vegetation Mapping in the Amazon Várzea Wetlands, Remote Sensing of Environment, 174, PP. 212-222.
 
 
Frohn, R.C., Autrey, B.C., Lane, C.R. & Reif, M., 2011, Segmentation and Object-Oriented Classification of Wetlands in a Karst Florida Landscape Using Multi-Season Landsat-7 ETM+ Imagery, International Journal of Remote Sensing, 32(5), PP. 1471-1489.
Hackman, K.O., Gong, P. & Wang, J., 2017, New Land-Cover Maps of Ghana for 2015 Using Landsat 8 and Three Popular Classifiers for Biodiversity Assessment, Int. J. Remote Sens., 38(14), PP. 4008-4021.
Holobâcă, I.H., Ivan, K. & Alexe, M., 2019, Extracting Built-Up Areas from Sentinel-1 Imagery Using Land-Cover Classification and Texture Analysis, International Journal of Remote Sensing, 40(20), PP. 8054-8069.
Ienco, D., Gaetano, R., Interdonato, R., Ose, K. & Minh, D.H.T., 2019, Combining Sentinel-1 and Sentinel-2 Time Series via RNN for Object-Based Land Cover Classification, In IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium (PP. 4881-4884), IEEE.
Jiao, L., Liu, Y. & Li, H., 2012, Characterizing Land-Use Classes in Remote Sensing Imagery by Shape Metrics, ISPRS Journal of Photogrammetry and Remote Sensing, 72, PP. 46-55.
Jung, R., Adolph, W., Ehlers, M. & Farke, H., 2015, A Multi-Sensor Approach for Detecting the Different Land Covers of Tidal Flats in the German Wadden Sea — A Case Study at Norderney, Remote Sensing of Environment, 170, PP. 188-202.
Karan, S.K. & Samadder, S.R., 2018, A Comparison of Different Land-Use Classification Techniques for Accurate Monitoring of Degraded Coal-Mining Areas, Environ. Earth Sci., 77(20), P. 2583.
Kavzoglu, T., & Colkesen, I. (2009). A kernel functions analysis for support vector machines for land cover classification. International Journal of Applied Earth Observation and Geoinformation, 11(5), 352-359.
Lee, J.S., 1981, Refined Filtering of Image Noise Using Local Statistics, Computer Graphics and Image Processing, 15(4), PP. 380-389.
Lee, C. A., Gasster, S. D., Plaza, A., Chang, C. I., & Huang, B. (2011). Recent developments in high performance computing for remote sensing: A review. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4(3), 508-527.
Li, G., Lu, D., Moran, E., Dutra, L. & 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, 70, PP. 26-38.
Lindquist, E.J. & D’Annunzio, R., 2016, Assessing Global Forest Land-Use Change by Object-Based Image Analysis, Remote Sens, 8(8), P. 678.
Maxwell, A.E., Warner, T.A. & Strager, M.P., 2016, Predicting Palustrine Wetland Probability Using Random Forest Machine Learning and Digital Elevation Data-Derived Terrain Variables, Photogrammetric Engineering & Remote Sensing, 82(6), PP. 437-447.
Mehravar, S., Dadrass Javan, F., Samadzadegan, F., Toosi, A., Moghimi, A., Khatami, R. & Stein, A., 2022, Varying Weighted Spatial Quality Assessment for High Resolution Satellite Image Pan-Sharpening, International Journal of Image and Data Fusion, 13(1), PP. 44-70.
Moradi, F., Javan, F.D. & Toosi, A., 2021, Tree Detection Using UAV Based Imagery System Based on Random Forest Classification, The 2nd International Electronic Conference on Forests — Sustainable Forests: Ecology, Management, Products and Trade.
Niu, X., & Ban, Y. (2013). Multi-temporal RADARSAT-2 polarimetric SAR data for urban land-cover classification using an object-based support vector machine and a rule-based approach. International journal of remote sensing, 34(1), 1-26.
Niculescu, S., Lardeux, C., & Hanganu, J. (2017). Synergy between Sentinel-1 radar time series and Sentinel-2 optical for the mapping of restored areas in Danube delta. Proceedings of the International Cartographic Association, 1.
Paneque-Gálvez, J., Mas, J.-F., Moré, G., Cristóbal, J., Orta-Martínez, M., Luz, A.C., Guèze, M., Macía, M.J. & Reyes-García, V., 2013, Enhanced Land Use/Cover Classification of Heterogeneous Tropical Landscapes Using Support Vector Machines and Textural Homogeneity, International Journal of Applied Earth Observation and Geoinformation, 23, PP. 372-383.
Petropoulos, G. P., Kalaitzidis, C., & Vadrevu, K. P. (2012). Support vector machines and object-based classification for obtaining land-use/cover cartography from Hyperion hyperspectral imagery. Computers & Geosciences, 41, 99-107.
Rahman, R. & Saha, S.K., 2008, Multi-Resolution Segmentation for Object-Based Classification and Accuracy Assessment of Land Use/Land Cover Classification Using Remotely Sensed data, Journal of the Indian Society of Remote Sensing, 36(2), PP. 189-201.
Rastner, P. (2014). The local glaciers and ice caps on Greenland: their mapping, separation from the ice sheet and their climate sensitivity (Doctoral dissertation, University of Zurich).
Rogan, J., Franklin, J., Stow, D., Miller, J., Woodcock, C., & Roberts, D. (2008). Mapping land-cover modifications over large areas: A comparison of machine learning algorithms. Remote Sensing of Environment, 112(5), 2272-2283.
Senf, C., Hostert, P. & van der Linden, S., 2012, Using MODIS Time Series and Random Forests Classification for Mapping Land Use in South-East Asia, 2012 IEEE International Geoscience and Remote Sensing Symposium, IEEE.
Shitole, S., De, S., Rao, Y.S., Mohan, B.K. & Das, A., 2015, Selection of Suitable Window Size for Speckle Reduction and Deblurring using SOFM in Polarimetric SAR Images, Journal of the Indian Society of Remote Sensing, 43(4), PP. 739-750.
Sonobe, R., Tani, H., Wang, X., Kobayashi, N. & Shimamura, H., 2014, Parameter Tuning in the Support Vector Machine and Random Forest and their Performances in Cross- and Same-Year Crop Classification Using TerraSAR-X, International Journal of Remote Sensing, 35(23), PP. 7898-7909.
Stromann, O., Nascetti, A., Yousif, O. & Ban, Y., 2020, Dimensionality Reduction and Feature Selection for Object-Based Land Cover Classification Based on Sentinel-1 and Sentinel-2 Time Series Using Google Earth Engine, Remote Sensing, 12(1), P. 76.
Talukdar, S., Singha, P., Mahato, S., Shahfahad, Pal, S., Liou, Y.-A. & Rahman, A., 2020, Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review, Remote Sens, 12(7), P. 1135.
Taşdemir, K., Milenov, P. & Tapsall, B., 2012, A Hybrid Method Combining SOM-Based Clustering and Object-Based Analysis for Identifying Land in Good Agricultural Condition, Computers and Electronics in Agriculture, 83, PP. 92-101.
Thompson, M., 1996, A Standard Land-Cover Classification Scheme for Remote-Sensing Applications in South Africa, South African Journal of Science, 92(1), PP. 34-42.
Toutin, T. (2004). Geometric processing of remote sensing images: models, algorithms and methods. International journal of remote sensing, 25(10), 1893-1924.
Xie, L., Zhang, H., Wang, C. & Shan, Z., 2015, Similarity Analysis of Entropy/Alpha Decomposition between HH/VV Dual- and Quad-Polarization SAR Data, Remote Sensing Letters, 6(3), PP. 228-237.
Xu, H., 2006, Modification of Normalised Difference Water Index (NDWI) to Enhance Open Water Features in Remotely Sensed Imagery, International Journal of Remote Sensing, 27(14), PP. 3025-3033.
Yesuph, A.Y. & Dagnew, A.B., 2019, Land Use/Cover Spatiotemporal Dynamics, Driving Forces and Implications at the Beshillo Catchment of the Blue Nile Basin, North Eastern Highlands of Ethiopia, Environ. Syst. Res., 8(1), P. 87.
Zhang, C. & Xie, Z., 2013, Object-Based Vegetation Mapping in the Kissimmee River Watershed Using HyMap Data and Machine Learning Techniques, Wetlands, 33(2), PP. 233-244.
Zhou, Y., Wang, H., Xu, F. & Jin, Y.Q., 2016, Polarimetric SAR Image Classification Using Deep Convolutional Neural Networks, IEEE Geoscience and Remote Sensing Letters, 13(12), PP. 1935-1939.
 
 
 
 

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