ترکیب تصاویر چندطیفی و SAR با قدرت تفکیک مکانی بالا به‌منظور آشکارسازی ساختمان‌ها در مناطق شهری

نوع مقاله : مقاله پژوهشی

نویسندگان

1 دانشجوی دکتری سنجش از دور، دانشگاه صنعتی خواجه نصیرالدین طوسی

2 دانشیار دانشکدة نقشه‌برداری، دانشگاه صنعتی خواجه نصیرالدین طوسی

3 استاد دانشکدة نقشه‌برداری، دانشگاه صنعتی خواجه نصیرالدین طوسی

چکیده

در این مقاله، به‌منظور رفع برخی محدودیت‌های شناسایی ساختمان در تصاویر چندطیفی، از دادة SAR به‌منزلة دادة مکمل استفاده می‌شود. در روش پیشنهادی، برای استفادة هم‌زمان از اطلاعات مفید در تصاویر رادار و چندطیفی، استراتژی مبتنی‌بر تلفیق تصاویر، با هدف شناسایی ساختمان، مطرح می‌شود. همچنین، ازآن‌جاکه انتخاب ویژگی نقش بسزایی در شناسایی و طبقه‌بندی عوارض دارد، اغلب روش‌های مرسوم و رایج در این زمینه، مانند الگوریتم ژنتیک، نیازمند داده‌های آموزشی‌اند؛ اما دردسترس‌نبودن همیشگی این نوع داده‌های آموزشی یکی از دغدغه‌های مهم محققان به‌شمار می‌آید. پس در این تحقیق، دو روش انتخاب ویژگی فیلترمبنا بررسی می‌شود تا مشخص شود آیا روش‌های یادشده می‌توانند، در مواقع لازم (نبودِ دادة آموزشی)، جایگزین الگوریتم ژنتیک شوند؟ بنابراین، در پژوهش حاضر، ابتدا بردار ویژگی‌ بهینه از تصویر چندطیفی و SAR، با سه روش MNF وPCA  و ژنتیک، تعیین و هریک جداگانه وارد هر دو طبقه‌بندی‌کنندة شبکة عصبی و SVM می‌شود. سپس به‌منظور رفع مشکلاتی، همچون تشابه طیفی پشت‌بام‌ها با پوشش آسفالت خیابان‌ها، در تصاویر چندطیفی و بهبود نتایج، دو تصویر چندطیفی و SAR در سطح ویژگی تلفیق می‌شود. در نهایت و در مرحلة بعدی، بهترین تصاویر طبقه‌بندی‌شده با شبکة عصبی و SVM، در تمامی بررسی‌های صورت‌گرفته تا به این مرحله، وارد تلفیق در سطح تصمیم‌گیری می‌شوند. نحوة تلفیق در سطح تصمیم‌گیری بدین‌صورت است که اطلاعات همسایگی هر پیکسل در قالب پنجرة مکانی متحرک در ابعاد متفاوت، با هدف تصمیم‌گیری درمورد ماهیت هر پیکسل، استفاده می‌شود. بنابراین، نتایج حاصل‌شده در این تحقیق، با صحت کلی و دقت ‌شناسایی ساختمان، به‌ترتیب 92.82% و 80.14% بیانگر عملکرد مناسب این روش است.

کلیدواژه‌ها


عنوان مقاله [English]

Integration of high spatial resolution SAR and multispectral images for building detection in urban areas

نویسندگان [English]

  • Maryam Teimouri 1
  • Mehdi Mokhtarzade 2
  • Mohammad Javad Valadan Zoej 3
1 Ph.d. Student of Remote Sensing, K. N. Toosi University of Technology
2 Associate Prof. of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology
3 Prof. of Photogrammetry and Remote Sensing, K. N. Toosi University of Technology
چکیده [English]

In this study, the SAR data is used as a supplementary data to overcome the limitations of the multispectral (MS) image in building detection. Therefore, the proposed method utilizes a multisensor data fusion to take the advantages of both MS and SAR data together. In addition, two different filter-based feature selection methods, MNF and PCA, are investigated as an alternative scenario when the training data is not accessible. In this respect, the optimum feature vector is selected using MNF, PCA and Genetic methods from MS and SAR data, separately. Thereafter, each selected feature vector is used to classify the images by implementing the support vector machine (SVM) and the artificial neural network classification methods. The experimental result shows that the PCA is able to select the feature vector without the need of training data as well as genetic algorithm. However, the MS classification result is poor where both roofs and streets are covered with asphalt. In this framework, the fusion of SAR and MS images in feature level was utilized to improve the classification results. Finally, to assign a label at the sample, a majority voting is calculated between the used classification methods results. However, according to the noisy result, using the neighborhood information in the form of a moving spatial window in different sizes is examined to determine the label of the central pixel more accurately. According to the experimental results, the overall accuracy and building detection accuracy are obtained 92.82% and 80.14%, respectively, which represent the satisfying performance of the proposed method.

کلیدواژه‌ها [English]

  • Building Detection
  • Feature Selection
  • SVM
  • Neural network
کابلی‌زاده، م.، 1387، طراحی و پیاده‌سازی یک سیستم اتوماتیک جهت استخراج عارضة ساختمان از تصاویر با قدرت تفکیک بالا با روش تلفیق داده‌های ارتفاعی با مدل Snake، پایان‌نامة کارشناسی ارشد، دانشگاه خواجه نصیرالدین طوسی، تهران.
مختاری، م.ح.، نجفی، ا.، 1393، مقایسة روش‌های طبقه‌بندی ماشین بردار پشتیبان و شبکة عصبی مصنوعی در استخراج کاربری‌های اراضی از تصاویر ماهواره‌ای لندست Tm، مجلة علوم‌و‌فنون کشاورزی و منابع طبیعی، علوم آب‌وخاک، سال نوزدهم، شمارة 72، تابستان 1394، ص. 45-35.
Anys, H., Bannari, A., He, D. & Morin, D., 1994, Texture Analysis for the Mapping of Urban Areas Using Airborne MEIS-II Images, Proceedings of the First International Airborne Remote Sensing Conference and Exhibition, PP. 231-245.
Benediktsson, J.A., Pesaresi, M. & Amason, K., 2003, Classification and Feature Extraction for Remote Sensing Images from Urban Areas Based on Morphological Transfor-mations, IEEE Transactions on Geoscience and remote Sensing, 41, PP. 1940-1949.
Bennett, A.J. & Blacknell, D., 2003, The Extraction of Building Dimensions from High Resolution SAR Imagery, Radar Conference, Proceedings of the International, 2003. IEEE, PP. 182-187.
Cote, M. & Saeedi, P., 2013, Automatic Rooftop Extraction in Nadir Aerial Imagery of Suburban Regions Using Corners and Variational Level Set Evolution, IEEE Transactions on Geoscience and Remote Sensing, 51, PP. 313-328.
Dong, Y., Chen, H., Yu, D., Pan, Y. & Zhang, J., 2011, Building Extraction from High Resolution SAR Imagery in Urban Areas, Geo-spatial Information Science, 14, PP. 164-168.
Dubois, C., Thiele, A. & Hinz, S., 2014, Building Detection in TanDEM-X Data, EUSAR 2014; 10th European Conference on Synthetic Aperture Radar, PP. 866-869.
Dubois, C., Thiele, A. & Hinz, S., 2016, Building Detection and Building Parameter Retrieval in InSAR Phase Images, ISPRS Journal of Photogrammetry and Remote Sensing, 114, PP. 228-241.
Gao, L., Zhao, B., Jia, X., Liao, W. & Zhang, B., 2017, Optimized Kernel Minimum Noise Fraction Transformation for Hyperspectral Image Classification, Remote Sensing, 9(6), P. 548.
Ghanbari, Z. & Sahebi, M.R., 2014, Improved IHS Algorithm for Fusing High Resolution Satellite Images of Urban Areas, Journal of the Indian Society of Remote Sensing, 42, PP. 689-699.
Green, A.A., Berman, M., Switzer, P. & Craig, M.D., 1988, A Transformation for Ordering Multispectral Data in Terms of Image Quality with Implications for Noise Removal, IEEE Transactions on Geoscience and Remote Sensing, 26, PP. 65-74.
 
Guo, R. & Zhu, X.X, 2014, High-Rise Building Feature Extraction Using High Resolution Spotlight TanDEM-X Data, EUSAR 2014; 10th European Conference on Synthetic Aperture Radar.
Haghighat, M., Zonouz, S. & Abdel-Mottaleb, M., 2013, Identification Using Encrypted Biometrics, International Conference on Computer Analysis of Images and Patterns, Springer, PP. 440-448.
Haralick, R.M. & Shanmugam, K., 1973, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, PP. 610-621.
Hill, R., Moate, C. & Blacknell, D., 2008, Estimating Building Dimensions from Synthetic Aperture Radar Image Sequences, IET Radar, Sonar & Navigation, 2, PP. 189-199.
Jin, X. & Davis, C.H., 2005, Automated Building Extraction from High-Resolution Satellite Imagery in Urban Areas Using Structural, Contextual, and Spectral Information, EURASIP Journal on Advances in Signal Processing, PP. 1-11.
Khesali, E., Zoej, M.J.V., Mokhtarzade, M., Dehghani , M., 2016, Semi Automatic Road Extraction by Fusion of High Resolution Optical and Radar Images, J Indian Soc Remote Sens, 44, PP. 21-29.
Lefèvre, S., Weber, J. & Sheeren, D., 2007, Automatic Building Extraction in VHR Images Using Advanced Morphological Operators, Urban Remote Sensing Joint Event, 2007. IEEE, PP. 1-5.
Liu, C., Wang, W., Zhao, Q., Shen, X. & Konan, M, 2017, A New Feature Selection Method Based on a Validity Index of Feature Subset, Pattern Recognition Letters, 92, PP. 1-8.
Liu, W., Yamazaki, F., Adriano, B., Mas, E. & Koshimura, S., 2014, Development of Building Height Data in Peru from High-Resolution SAR Imagery, Journal of Disaster Research, Vol. 9, P. 1043.
Maghsoudi, Y., 2011, Analysis of Radarsat-2 Full Polarimetric Data for Forest Mapping, Ph.D. Thesis, Department of Geomatics Engineering, University of Calgary.
Michaelsen, E., Soergel, U. & Thoennessen, U., 2006, Perceptual Grouping for Automatic Detection of Man-Made Structures in High-Resolution SAR Data, Pattern Recognition Letters, 27, PP. 218-225.
Moreira, A., 1991, Improved Multilook Tech-niques Applied to SAR and SCANSAR Imagery, IEEE Transactions on Geoscience and remote Sensing, 29(4), PP. 529-534.
Ojaghi, S., Ebadi, H. & Ahmadi, F.F., 2015, Using Artificial Neural Network for Classification of High Resolution Remotely Sensed Images and Assessment of Its Performance Compared with Statistical Methods, American Journal of Engineering, Technology and Society, 2(11), PP. 1-8.
Pontius Jr., R.G. & Millones, M., 2011, Death to Kappa: Birth of Quantity Disagreement and Allocation Disagreement for Accuracy Assessment, International Journal of Remote Sensing, 32, PP. 4407-4429.
Poulain, V., Inglada, J., Spigai, M., Tourneret, J.-Y. & Marthon, P., 2011, High-Resolution Optical and SAR Image Fusion for Building Database Updating, IEEE Transactions on Geoscience and Remote Sensing, 49, PP. 2900-2910.
Shackelford, A.K. & Davis, C.H., 2003, A Combined Fuzzy Pixel-Based and Object-Based Approach for Classification of High-Resolution Multispectral Data over Urban Areas, IEEE Transactions on Geoscience and Remote Sensing, 41, PP. 2354-2363.
Simonetto, E., Oriot, H. & Garello, R., 2005, Rectangular Building Extraction from Stereoscopic Airborne Radar Images, IEEE Transactions on Geoscience and Remote Sensing, 43, PP. 2386-2395.
Sohn, G. & Dowman, I., 2001, Extraction of Buildings from High Resolution Satellite Data, Automated Extraction of Man-Made Objects from Aerial and Space Images (III), Balkema Publishers, Lisse, PP. 345-355.
Sportouche, H., Tupin, F. & Denise, L., 2011, Extraction and Three-Dimensional Recon-struction of Isolated Buildings in Urban Scenes from High-Resolution Optical and SAR Spaceborne Images, IEEE Transac-tions on Geoscience and Remote Sensing, 49, PP. 3932-3946.
Tarabalka, Y., Benediktsson, J.A. & Chanussot, J., 2009, Spectral-Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques, IEEE Transactions on Geoscience and Remote Sensing, 47, PP. 2973-2987.
Taubenböck, H., Esch, T., Wurm, M., Roth, A. & Dech, S., 2010, Object-Based Feature Extraction Using High Spatial Resolution Satellite Data of Urban Areas, Journal of Spatial Science, 55, PP. 117-132.
Teimouri, M., Mokhtarzade, M. & Valadan Zoej, M.J., 2016, Optimal Fusion of Optical and SAR High-Resolution Images for Semiautomatic Building Detection, GIScience & Remote Sensing, 53, PP. 45-62.
Tison, C., Tupin, F. & Maitre, H., 2004, Retrieval of Building Shapes from Shadows in High Resolution SAR Interferometric Images, 2004 IEEE International Geoscience and Remote Sensing Symposium.
Tupin, F., 2003, Extraction of 3D Information Using Overlay Detection on SAR Images, 2nd GRSS/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas, Berlin, Germany, 22-23 May 2003.
Tupin, F., Maitre, H., Mangin, J.-F., Nicolas, J.-M. & Pechersky, E., 1998, Detection of Linear Features in SAR Images: Application to Road Network Extraction, IEEE Transactions on Geoscience and Remote Sensing, 36, PP. 434-453.
Tupin, F. & Roux, M., 2003, Detection of Building Outlines Based on the Fusion of SAR and Optical Features, ISPRS Journal of Photogrammetry and Remote Sensing, 58, PP. 71-82.
Turker, M. & Koc-San, D., 2015, Building Extraction from High-Resolution Optical Spaceborne Images Using the Integration of Support Vector Machine (SVM) Classification, Hough Transformation and Perceptual Grouping, International Journal of Applied Earth Observation and Geoinformation, 34, PP. 58-69.
Turlapaty, A., Gokaraju, B., Du, Q., Younan, N.H. & Aanstoos, J.V., 2012, A Hybrid Approach for Building Extraction from Spaceborne Multi-Angular Optical Imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5, PP. 89-100.
Ulaby, F.T., Moore, R.K. & Fung, A.K., 1986, Microwave Remote Sensing- Active and Passive, Volume I- Microwave Remote Sensing Fundamentals and Radiometry, Artech House Publishers.
Ünsalan, C. & Boyer, K.L., 2005, A System to Detect Houses and Residential Street Networks in Multispectral Satellite Images, Computer Vision and Image Understanding, 98, PP. 423-461.
Wang, J., Yang, X., Qin, X., Ye, X. & Qin, Q., 2015, An Efficient Approach for Automatic Rectangular Building Extraction from Very High Resolution Optical Satellite Imagery, IEEE Geoscience and Remote Sensing Letters, 12, PP. 487-491.
Wang, Y., Tupin, F., Han, C. & Nicolas, J.-M., 2008, Building Detection from High Resolution POLSAR Data by Combining Region and Edge Information, International Geoscience and Remote Sensing Symposium, 2008. IEEE, IV-153-IV-156.
Wegner, J.D., Hansch, R., Thiele, A. & Soergel, U., 2011, Building Detection from One Orthophoto and High-Resolution InSAR Data Using Conditional Random Fields, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 4, PP. 83-91.
Zhai, W., Shen, H., Huang, C. & Pei, W., 2016, Fusion of Polarimetric and Texture Information for Urban Building Extraction from Fully Polarimetric SAR Imagery, Remote Sensing Letters, 7, PP. 31-40.
Zhang, Y., 1999, Optimisation of Building Detection in Satellite Images by Combining Multispectral Classification and Texture Filtering, ISPRS Journal of Photogrammetry and Remote Sensing, 54, PP. 50-60.
Zhao, L., Zhou, X. & Kuang, G., 2013, Building Detection from Urban SAR Image Using Building Characteristics and Contextual Information, EURASIP Journal on Advances in Signal Processing, 2013, P. 1.
Zheng, L., Wan, L., Huo, H. & Fang, T., 2014, A Noise Removal Approach for Object-Based Classification of VHR Imagery via Post-Classification, 2014 International Conference on Audio, Language and Image Processing, 7-9 July, Shanghai, China.