شبكة اكتشاف التغير التوأمية Nested-UNet (SNU-PS) القائمة على فضاء احتمالية التصنيف اللاحقة

  • role: First author第一作者
  • Affiliation:

    State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

    Beijing Municipal Engineering and Technology Center for Land Surface Remote Sensing Data Products, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

  • Email:zhuchuanhai@mail.bnu.edu.cn
  • Introduction:朱传海,研究方向为遥感变化检测与灾情监测。E-mail: zhuchuanhai@mail.bnu.edu.cn
ZHU Chuanhai,  
  • role: Corresponding author通信作者
  • Affiliation:

    State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

    Beijing Municipal Engineering and Technology Center for Land Surface Remote Sensing Data Products, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

  • Email:chenxuehong@bnu.edu.cn
  • Introduction:陈学泓,研究方向为遥感数据融合、变化检测。E-mail: chenxuehong@bnu.edu.cn
CHEN Xuehong*,  
  • Affiliation:

    State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

    Beijing Municipal Engineering and Technology Center for Land Surface Remote Sensing Data Products, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

CHEN Jin,  
  • Affiliation:

    State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

    Beijing Municipal Engineering and Technology Center for Land Surface Remote Sensing Data Products, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

YUAN Yuheng,  
  • Affiliation:

    State Key Laboratory of Remote Sensing Science, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

    Beijing Municipal Engineering and Technology Center for Land Surface Remote Sensing Data Products, Faculty of Geography Science, Beijing Normal University, Beijing 100875, China

TANG Kai

ملخص

في السنوات الأخيرة، أظهر التعلم العميق إمكانات كبيرة في مهام اكتشاف التغير في الصور الاستشعار عن بعد متعددة الفترات الزمنية. تعد العينات التدريبية الكافية شرطًا أساسيًا لكي تتمكن تقنيات التعلم العميق من استخراج ميزات التغير في الصور الاستشعار عن بعد بفعالية، ومع ذلك فإن مجموعات البيانات الموسومة المتاحة الحالية لا تلبي احتياجات الكشف عن أنواع التغير المختلفة في التطبيقات العملية. نظرًا لأن تغيرات غطاء الأرض عادةً ما تشغل جزءًا صغيرًا فقط من المنطقة، فإن العينات المتاحة للتغيرات غالبًا ما تكون قليلة العدد، وتواجه مشكلة عدم التوازن الجسيم مقارنةً بعينات عدم التغير. لذلك، فإن كيفية تدريب شبكات اكتشاف التغير بفعالية في ظل قلة العينات وعدم توازنها هو تحدٍ بحاجة إلى اختراق. مقارنة بعينات اكتشاف التغير، فإن الحصول على عينات تصنيف غطاء الأرض في فترة زمنية واحدة أسهل بكثير؛ وبفضل دعم عينات التصنيف، يمكن للشبكة المصنفة التي تم تدريبها بشكل كافٍ لغطاء الأرض أن توفر ميزات أولوية هامة لاكتشاف التغير. بناءً على ذلك، يقترح هذا البحث شبكة اكتشاف التغير التوأمية Nested-UNet القائمة على فضاء احتمالية التصنيف اللاحقة SNU-PS (Siamese Nested-UNet for change detection in Posterior Probability Space)، والتي تقلل من الاعتماد على عينات اكتشاف التغير من خلال دمج معلومات احتمال تصنيف غطاء الأرض في فترتين زمنيتين. أولاً، يستخدم هذا النهج عينات تصنيف غطاء الأرض لتدريب شبكة عالية الدقة HRNet (High-Resolution Network) للحصول على احتمالات تصنيف الأشياء في الصور من فترتين زمنيتين؛ ثم تُدخل خرائط الاحتمالية اللاحقة إلى شبكة SNU التوأمية لاكتشاف التغير للحصول على نتائج الكشف. تظهر نتائج الاختبارات على مجموعتي البيانات SpaceNet7 وHRSCD أن SNU-PS يمكنها استغلال المعلومات الدلالية لغطاء الأرض بشكل كامل، وتحافظ على دقة اكتشاف التغير مستقرًا عند مستويات مختلفة من عدد عينات التدريب؛ كما تتمتع بدقة استقرار أعلى في الكشف مقارنة مع الطرق PCC (Post Classification Comparison) وCVAPS (Change-vector analysis in posterior probability space) وشبكات اكتشاف التغير المختلفة مثل SNU وFC-EF وBIT وPCFN، وخاصة عند قلة العينات، حيث تكون الميزات أكثر وضوحًا. لذا، فإن SNU-PS المقترحة لديها آفاق تطبيق أفضل في مهام اكتشاف التغير في ظل ظروف قلة العينات.

مفهوم

غطاء الأرض; اكتشاف التغير; التعلم العميق; قلة العينات; عدم توازن العينات; شبكة تقسيم دلالي; شبكة توأمية; احتمالية لاحقة

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