دوره 21، شماره 3 - ( 10-1403 )
جلد 21 شماره 3 صفحات 84-69 |
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Jahani S A, Mohebbi K, Zamani Boroujeni F. Improving Scene Recognition in Remote Sensing Using Deep Learning and Feature Selector. JSDP 2024; 21 (3) : 3
URL: http://jsdp.rcisp.ac.ir/article-1-1398-fa.html
URL: http://jsdp.rcisp.ac.ir/article-1-1398-fa.html
جهانی سید علی، محبی کیوان، زمانی بروجنی فرساد. بهبود تشخیص صحنه در سنجش از راهدور با استفاده از یادگیری عمیق و انتخاب‌گر ویژگی. پردازش علائم و داده‌ها. 1403; 21 (3) :69-84
استادیار گروه مهندسی کامپیوتر، دانشگاه آزاد اسلامی، واحد اصفهان (خوراسگان)، اصفهان، ایران
چکیده: (804 مشاهده)
تصاویر سنجش‌‌ازدور به‌عنوان منبع‌داده با ارزش می‌تواند در اندازه‌گیری و مشاهده ساختارهای دقیق در سطح زمین کمک کند. هدف این پژوهش ارائه راهکار تشخیص صحنه در سنجش از راهدور با به‌کارگیری روش‌های یادگیری عمیق است. برای رفع محدودیت‌های روش‌های پیشین، یک رویکرد ترکیبی استخراج ویژگی مطرح شده‌است که در آن دو نوع ویژگی عمیق محلی و سراسری و یک نوع محلی دستی با یکدیگر ترکیب شدهاند. برای استخراج ویژگی‌ها یک شبکه کانولوشن پیش‌آموزش‌دیده با بیست لایه تمام‌متصل پیشنهاد شده‌است؛ همچنین یک مرحله انتخاب ویژگی با استفاده از دو دسته الگوریتم‌های پالایه و بستهبند در این مدل تعبیه شده‌است؛ در نهایت با استفاده از الگوریتم‌های طبقه‌بندی مختلف تشخیص صحنه انجام می‌شود. ارزیابی راهکار پیشنهادی برای مجموعه‌دادههای UCM، AID ،RSSCN7 و NWPU-RESISC45 به‌ترتیب دقت 27/99% ، 91/97% ، 09/99% و 09/93% را کسب کرده‌است.
شماره‌ی مقاله: 3
نوع مطالعه: پژوهشي |
موضوع مقاله:
مقالات پردازش تصویر
دریافت: 1402/6/18 | پذیرش: 1403/5/31 | انتشار: 1403/10/28 | انتشار الکترونیک: 1403/10/28
دریافت: 1402/6/18 | پذیرش: 1403/5/31 | انتشار: 1403/10/28 | انتشار الکترونیک: 1403/10/28
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