نوع مقاله : علمی - پژوهشی

نویسندگان

1 دانشجوی دکتری نقشه­ برداری گروه فتوگرامتری و سنجش از دور دانشکده مهندسی ژئودزی و ژئوماتیک

2 استاد گروه فتوگرامتری و سنجش از دور دانشکده مهندسی ژئودزی و ژئوماتیک، دانشگاه صنعتی خواجه نصیرالدین طوسی (عضو قطب علمی فناوری اطلاعات مکانی)

3 کارشناسی ارشد ژئودزی، دانشگاه آزاد اسلامی واحد شاهرود

چکیده

طبقه‌بندی پوشش اراضی در تصاویر سنجش‌ازدور، یکی از پرکاربردترین روش‌های استخراج اطلاعات مکانی است که می‌تواند با تولید کلاس‌های تصویری عوارض سطح زمین به‌منظور اتوماسیون و تسریع در جهت رفع نیازهای اساسی برای در اختیار داشتن اطلاعات مکانی بهنگام از منابع، با هدف مدیریت، ساماندهی و بهره‌برداری از محیط مفید واقع شود. به دلیل مشابهت رفتار پیکسل‌ها، طبقه‌بندی تصاویر هوایی در مناطق پیچیده و متراکم شهری، صرفاً با استفاده از اطلاعات طیفی و بافتی منجر به ناکارآمدی طبقه‌بندی می‌شود. به عبارتی در طبقه‌بندی رایج، عمدتا با استفاده از خصوصیات طیف و ویژگی‌های پیکسل‌های تصویر، به شناسایی عوارض و کلاس­ها پرداخته می‌شود. درصورتی‌که بتوان تطابق مکانی و مفهومی پیکسل­ها را نیز در نظر گرفت، به این ترتیب می­توان تمایز بیشتری بین کلاس­های تصویری قائل شد و فرآیند ماشینی را به تفسیر ذهنی و انسانی نزدیک کرده و بر کارایی سیستم افزود. تمرکز اصلی تحقیق حاضر، استفاده از مفاهیم سیستم­های خبره در طبقه­بندی، به‌منظور آنالیز شئ‏گرای تصاویر در سطوح مقیاس کلاسی است. به همین دلیل، با وارد نمودن قوانین دانش‌پایه به‌منظور کنترل هدفمند و قانونمندسازی روند توأمان قطعه­بندی و تفسیر تصویر، با در نظر گرفتن ویژگی­های هندسی کلاس­های هدف، بهبود دقت را منجر شود. برای بررسی کارایی تکنیک پیشنهادی، ارزیابی و مقایسه روش پیشنهادی با چند روش دیگر بر روی تصاویر ماهواره­ا‌ی IRS در منطقه شهری جزیره کیش صورت پذیرفته است. نتایج حاصل از این تحقیق، نشان می‌دهد ویژگی‌های هندسی و مفهومی می‌توانند به‌عنوان ­منبع اطلاعاتی مکمل، سبب بهبود نتایج طبقه­بندی در منطقه شهری با عوارض ناهمگون طیفی شوند. به‌طوریکه در بررسی مورد اشاره، صحت کلی و ضریب کاپا به ترتیب 8 درصد و 11/5 درصد افزایش پیدا کرده­اند.

کلیدواژه‌ها

عنوان مقاله [English]

Object based interpretation of high spatial remote sensing images based on knowledge-based systems

نویسندگان [English]

  • Abbas Kiani 1
  • Hamid Ebadi 2
  • Hekmat allah Khanlou 3

1 Geomatics Engineering Faculty, K.N.Toosi University of Technology

2 Professor in Geomatics Engineering Faculty, K.N.Toosi University of Technology

3 Department of Geomatics Engineering, Islamic Azad University, Shahrood, Iran

چکیده [English]

Land cover classification in remote sensing imagery is one of the most widely used spatial information extraction methods, which can facilitate generating object imagery classes of the ground surface in order to automate and accelerate meeting the basic needs of management, organization, and exploitation of the environment. Due to the similar behavior of pixels, remote-sensing image classification using merely the spectral and textural information would lead to inefficiency in the classification. In fact, in classification process, objects are commonly identified using spectral properties of image pixels. If the spatial and conceptual properties are also considered, it causes to a better distinction between image classes and closes the machine process to human interpretation and adds to the system's performance. The present research is mainly focused on the use of interactive segmentation and interpretation processes with respect to the geometry of the image classes. The accuracy of the results have improved by introducing the knowledge-based rules to control and regulate the interactive process, taking into account the geometric properties of target classes. To evaluate the efficiency of the proposed method, the results were evaluated and compared with some of the other methods on IRS satellite images in an urban area. The results showed that geometric and conceptual features as a complementary information source, improve classification results in the urban area with heterogeneous spectral effects. Overall, the proposed hybrid technique improved overall accuracy and Kappa coefficient by 8% and 11.5%, respectively.

کلیدواژه‌ها [English]

  • Classification
  • High spatial image
  • Knowledge based system
  • Object based interpretation
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