كشف ظلال الأرض في صور الاستشعار عن بعد عالية الدقة استنادًا إلى افتراض الاتجاه المسبق للظلال

  • role: First author第一作者
  • Affiliation:

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

  • Email:qikunlun@cug.edu.cn
  • Introduction:E-mail qikunlun@cug.edu.cn
QI Kunlun1,  
  • Affiliation:

    China Mobile (Hangzhou) Information Technology Co., Ltd., Hangzhou 311100, China

MA Xinyue2,  
  • Affiliation:

    School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, China

JIN Zhun1,  
  • Affiliation:

    National Engineering Research Center of Geographic Information System, Wuhan 430074, China

QING Yaxian3,  
  • role: Corresponding author通信作者
  • Affiliation:

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

  • Email:lizhenqiang@cug.edu.cn
  • Introduction:E-mail lizhenqiang@cug.edu.cn
LI Zhenqiang4*,  
  • Affiliation:

    State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China

YANG Chao4,  
  • Affiliation:

    National Engineering Research Center of Geographic Information System, Wuhan 430074, China

WU Huayi3

ملخص

كانت تكلفة تسمية الظلال الأرضية مكلفة وصعبة التغطية بشكل شامل للمعلومات الغنية المتضمنة في صور الاستشعار عن بعد عالية الدقة. ندرة عينات التدريب، تقيد بشدة أداء نموذج التعلم الإشرافي. لمواجهة المشكلة المذكورة أعلاه، قدمت هذه الورقة طريقة للكشف عن ظلال الأرض في صور الاستشعار عن بعد عالية الدقة استنادًا إلى افتراض الاتجاه المسبق للظلال. استقصت هذه الطريقة فعالية الاتجاه المسبق للظلال في تعبير السمات الدلالية العالية لظلال الأرض من صور الاستشعار عن بعد، وأسست أساسًا لمهمة مساعدة في كشف ظلال الأرض في صور الاستشعار عن بعد استنادًا إلى اتجاهه. قدمت هذه الورقة آلية لمعالجة الضوضاء المستقلة من التغييرات في الاتجاه واستراتيجية تعزيز بيانات الكشف ذات الاشراف الذاتي للكشف عن الظلال. عززت هذه الطريقة قدرة الشبكة العصبية العميقة على تعلم السمات الرئيسية للظلال الأرضية. أظهرت النتائج التجريبية على مجموعة بيانات AISD أن هذه الطريقة تحسنت بشكل ملحوظ دقة كشف الظلال الأرضية باستخدام عدد قليل من التسميات فقط، وأن حدود الظلال الأرضية أصبحت أكثر نعومة ومنتظمة وأقرب إلى الواقع الأرضي الحقيقي.

مفهوم

كشف الظلال؛ التعلم الإشرافي الذاتي؛ تعزيز البيانات؛ افتراض الاتجاه المسبق للظلال؛ صور الاستشعار عن بعد عالية الدقة

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