이전|다음

분류 후행 확률 공간 기반 쌍둥이 Nested-UNet(SNU-PS) 변화 감지 네트워크

  • 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

추상적인

최근 몇 년간 딥러닝은 다시기 원격탐사 영상 변화 감지 작업에서 엄청난 잠재력을 보여주고 있습니다. 충분한 학습 샘플은 딥러닝 기술이 원격탐사 영상 변화 특징을 효과적으로 추출할 수 있는 중요한 전제 조건입니다. 그러나 현재 제한적인 공개 라벨 데이터셋은 실제 응용에서 다양한 변화 유형 검출 요구를 충족하지 못하고 있습니다. 지표 피복 변화는 일반적으로 일부 영역에만 국한되기 때문에 얻을 수 있는 변화 샘플은 수가 매우 적으며, 변화가 없는 샘플과 비교할 때 심각한 불균형 문제가 존재합니다. 따라서 소수 샘플과 불균형 샘플 상황에서 변화 감지 네트워크를 효과적으로 학습시키는 방법은 시급히 해결해야 할 난제입니다. 변화 감지 샘플과 비교하여 단일 시기 지표 피복 분류 샘플 획득은 훨씬 수월하며, 분류 샘플의 지원 하에 충분히 학습된 지표 피복 분류 네트워크는 변화 감지에 중요한 사전 특징을 제공할 수 있습니다. 이에 본 논문은 분류 후행 확률 공간 기반의 쌍둥이 Nested-UNet 변화 감지 네트워크 SNU-PS(Siamese Nested-UNet for change detection in Posterior Probability Space)를 제안하며, 두 시기의 지표 피복 분류 후행 확률 정보를 결합하여 변화 감지 샘플 의존도를 줄입니다. 본 방법은 우선 지표 피복 분류 샘플을 활용해 고해상도 네트워크 HRNet(High-Resolution Network)을 학습시켜 두 시기 영상의 지물 분류 후행 확률을 얻고, 후행 확률 영상을 쌍둥이 Nested-UNet 변화 감지 네트워크 SNU(Siamese Nested-UNet for change detection)에 입력해 변화 감지 결과를 도출합니다. SpaceNet7 및 HRSCD 데이터셋에서의 테스트 결과는 SNU-PS가 지표 피복의 의미론적 정보를 충분히 활용하여 다양한 변화 감지 학습 샘플 수 수준에서 안정적인 변화 감지 정확도를 유지함을 보여줍니다. 분류 후 비교 PCC(Post Classification Comparison), 후행 확률 공간 기반 변화 벡터 분석 CVAPS(Change-vector analysis in posterior probability space) 및 다양한 유형의 변화 감지 네트워크(SNU, FC-EF, BIT, PCFN)와 비교할 때 SNU-PS는 더 높고 안정적인 변화 감지 정확도를 가지며, 특히 샘플 수가 부족할 때 그 장점이 더욱 뚜렷합니다. 따라서 본 논문에서 제안한 SNU-PS는 소수 샘플 환경에서의 변화 감지 작업에 더 나은 적용 전망을 가집니다.

키워드

지표 피복; 변화 감지; 딥러닝; 소수 샘플; 샘플 불균형; 의미론적 분할 네트워크; 쌍둥이 네트워크; 후행 확률

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