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
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