نوع مقاله : علمی - پژوهشی
نویسندگان
دانشیار دانشکده فناوری اطلاعات و مهندسی کامپیوتر، دانشگاه شهید مدنی آذربایجان، ایران
چکیده
تصاویر هوایی ثبت‌شده توسط ماهواره‌ها و یا پهپاد‌ها، معمولا شامل نواحی مربوط به منابع طبیعی و نواحی حاوی سازه‌های ساخت بشر است. با تفکیک این نواحی از یکدیگر، قادر خواهیم بود اطلاعات مهمی نظیر آرایش ساختاری سطوح و شکل آنها را استخراج و نقشه‌های هواییِ برچسب‌دار ایجاد کنیم. دست‌یابی به چنین اطلاعاتی می‌تواند کاربرد‌های بسیار مفیدی در زمینه‌های نظامی، شهری و زیست محیطی داشته باشد. از آنجایی‌که پردازش حجم عظیمی از تصاویر به‌دست آمده از ماهواره‌ها و پهپاد‌ها بصورت دستی امکان‌پذیر نیست، لذا استفاده از روش‌های خودکارِ مبتنی بر هوش مصنوعی در این حوزه، بسیار مورد توجه قرار گرفته است. تاکنون پژوهش‌های متعددی در این‌باره انجام شده است که از اهم آنها می‌توان به شناسایی ساختمان‌ها، وسایل نقلیه، جاده‌ها و همچنین تشخیص ساختار پوشش گیاهی در تصاویر هوایی اشاره کرد. در این مقاله، قصد داریم با معرفی مجموعه‌ای از ویژگی‌های آماری مبتنی بر رنگ که به سادگی قابل استخراج از تصاویر هوایی هستند و با استفاده از یک مدل مبتنی بر یادگیری، راهکاری کارآمد برای تشخیص دقیق و سریع سازه‌های ساخت بشر و تفکیک آنها از منابع طبیعی ارایه دهیم. نتایج آزمایش‌های انجام گرفته بر روی بانک تصاویر ماساچوست که به‌صورت عمومی، قابل دسترس است، نشان دهنده دقت مناسب و سرعت عمل بالای راهکار پیشنهادی است. به‌طوری‌که، دقت و میانگین سرعت پردازش به‌دست آمده به‌ترتیب برابر با ٪۰۷/۹۰ و 96/0 ثانیه است.
کلیدواژه‌ها
عنوان مقاله [English]
Man-Made Object Detection in Aerial Images Using Color Statistical Features and Machine Learning
نویسندگان [English]
- Naser Farajzadeh
- Mehdi Hashemzadeh
Associate Professor, Faculty of IT and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran
چکیده [English]
Generally, the photos captured by drones and satellites include both natural scenes and man-made objects. Having these two categories classified, we will be able to extract important information from aerial scenes such as the shapes and the alignments of the structures and then, create labeled aerial images accordingly. Obtaining such information is of great interest in, for example, military, urban, and environmental protection applications. However, due to a huge amount of data that is collected in form of images, it seems that manually processing of such data is impossible. Therefore, employing automatic techniques based on artificial intelligence has become more on demand. There are numerous researches on this topic from which detection of buildings, vehicles, roads, and vegetation are of more interest. In this paper, we aim to introduce a method to detect man-made objects in aerial images based on a new set of color statistical features, which can be easily extracted, together with a learning model. Experimental results on a publicly available dataset, Massachusetts dataset, have shown promising results in terms of both accuracy and processing time; the accuracy and the average processing time are 90.07% and 0.96 seconds, respectively.
کلیدواژه‌ها [English]
- aerial images
- natural scene
- man-made objects
- Machine Learning
- statistical features
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