이전|다음

결합 중심 차이 특성 및 스펙트럼-공간 주의 기반 고분광 영상 변화 탐지 방법

  • role： First author第一作者
  • Affiliation：

    College of Computer Science, Chongqing University, Chongqing 401331, China

  • Email：zhoutianyuan1016@163.com
  • Introduction：周天媛，研究方向为高光谱变化检测。E-mail： zhoutianyuan1016@163.com
ZHOU Tianyuan1,  
  • Affiliation：

    College of Optoelectronic Engineering, Chongqing University, Chongqing 401331, China

LIU Jiamin2,  
  • Affiliation：

    School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China

GUO Tan3,  
  • Affiliation：

    College of Computer Science, Chongqing University, Chongqing 401331, China

FU Chuan1,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Computer Science, Chongqing University, Chongqing 401331, China

  • Email：luoflyn@163.com
  • Introduction：罗甫林，研究方向为遥感智能解译。E-mail： luoflyn@163.com
LUO Fulin1*

추상적인

다중 시점 고분광 영상은 풍부한 스펙트럼 밴드와 영상 세부 정보로 인해 변화 탐지에 널리 사용된다. 감독 학습 기반의 고분광 변화 탐지 알고리즘은 종종 많은 라벨 샘플에 의존하여 라벨링 비용이 높아진다. 이를 위해 본 논문에서는 고분광 영상 변화 탐지를 위한 JCDS²AN(결합 중심 차이 특성 및 스펙트럼-공간 주의 네트워크)을 제안한다. 이 네트워크는 제한된 샘플 하에서 변화 특성 변동을 완화하고, 제한된 라벨 샘플로 대표성 있는 변화 특성을 학습할 수 있다. 본 논문에서 제안한 JCDS²AN은 공간 및 스펙트럼의 다중 스케일 특성을 포착하기 위해 다중 스케일 스펙트럼-공간 주의 블록을 설계하였고, 차이 특성에 의해 유도된 차등 중심 픽셀 교환 전략을 제안하여 차이 특성과 두 시점 특성 간 효율적인 정보 상호작용을 실현하였다. 또한 다른 8가지 고분광 변화 탐지 방법들과 3개의 공개 데이터셋에서 시각적 및 정량적 실험을 통해 비교 검증하였다. 검증 결과 본 논문에서 제안한 JCDS²AN이 다른 고분광 변화 탐지 방법보다 우수함을 보여주었다.

키워드

고분광 영상; 원격 탐사 영상; 변화 탐지; 다중 스케일 특징; 차이 특성 유도; 중심 픽셀

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