Обзор обнаружения изменений в дистанционном зондировании на основе глубокого обучения: библиометрический анализ

  • role: First author第一作者
  • Affiliation:

    School of Electrical and Information Engineering, Hunan University, Changsha 410082, China

  • Email:binyang@hnu.edu.cn
  • Introduction:E-mailbinyang@hnu.edu.cn
YANG Bin1,  
  • Affiliation:

    School of Electrical and Information Engineering, Hunan University, Changsha 410082, China

MAO Yin1,  
  • Affiliation:

    Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China

CHEN Jin2,  
  • Affiliation:

    National Satellite Ocean Application Service, Beijing 100081, China

LIU Jianqiang3,  
  • Affiliation:

    School of Geosciences and Info-Physics, Central South University, Changsha 410083, China

CHEN Jie4,  
  • role: Corresponding author通信作者
  • Affiliation:

    School of Land Science and Technology, China University of Geosciences (Beijing), Beijing 100084, China

  • Email:kaiyan@bnu.edu.cn
  • Introduction:E-mailkaiyan@bnu.edu.cn
YAN Kai5*

реферат

Обнаружение изменений с помощью дистанционного зондирования позволяет получать информацию об изменениях земной поверхности и имеет важное значение для понимания взаимодействия человека и природы, а также для продвижения устойчивого развития. С развитием технологий дистанционного зондирования и быстрого прогресса в компьютерных науках гиперспектральные, временные и пространственные изображения высокого разрешения широко применяются, что способствовало развитию обнаружения изменений на основе глубокого обучения и успешному применению в различных областях. В отличие от традиционных методов обнаружения изменений дистанционного зондирования, методы на основе глубокого обучения извлекают глубокие дифференциальные признаки изображений без необходимости создания инженерии признаков, повышая точность и эффективность обнаружения. В статье с помощью библиометрического анализа комплексно рассматривается текущее состояние и горячие темы исследований в данной области, выявлено, что обнаружение изменений на основе глубокого обучения под руководством отечественных учреждений и ученых быстро развивается и приносит значительные результаты. Большинство результатов основано на изображениях высокого разрешения и сетях CNN с успешным применением в выявлении изменений в землепользовании/покрытии и строительстве. В основе статьи представлена классификация методов обнаружения изменений на основе глубокого обучения по трем уровням детализации: пиксель, объект и сцена, описываются процессы извлечения признаков и последующего анализа сетей для каждого уровня, при этом методы, основанные на объектах и сценах, обладают преимуществами. Наконец, обобщены текущие вызовы и возможные направления развития. В связи с развитием платформ дистанционного зондирования и увеличением требований к применению, многоформатное гетерогенное обнаружение изменений является будущим трендом. Кроме того, методы глубокого обучения должны преодолевать проблемы с неидеальными образцами, уделять внимание получению разнообразной информации об изменениях и продвигать широкое применение обнаружения изменений.

ключеви́че слова́

дистанционное зондирование;обнаружение изменений;глубокое обучение;библиометрия;классификация методов;вызовы и развитие;обзор

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