이전|다음

약지도학습 기반 원격탐사 영상 저비용 세밀 해석 방법

  • role： First author第一作者
  • Affiliation：

    College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China

  • Email：chenmanhjdx@163.com
  • Introduction：陈满，研究方向为遥感图像处理。E-mail： chenmanhjdx@163.com
CHEN Man,  
  • Affiliation：

    College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China

HUANG Yongjie,  
  • Affiliation：

    College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China

XU Lei,  
  • role： Corresponding author通信作者
  • Affiliation：

    College of Command and Control Engineering, Army Engineering University of PLA, Nanjing 210007, China

  • Email：panzhisong@aeu.edu.cn
  • Introduction：潘志松，研究方向为模式识别与机器学习。E-mail： panzhisong@aeu.edu.cn
PAN Zhisong*

추상적인

원격탐사 영상의 인스턴스 분할은 관심 객체의 목표 수준 위치 지정과 픽셀 수준 분류를 동시에 수행할 수 있는 중요한且도 도전적인 작업입니다. 현재 대부분의 원격탐사 영상 인스턴스 분할 방법은 정밀한 픽셀 수준 주석에 의존하며, 그 제작 비용이 매우 높습니다. 또한 원격탐사 영상의 혼재된 전경과 배경 및 복잡한 객체 윤곽선도 분할 난이도를 높입니다. 이러한 도전에 대응하기 위해 본 논문은 원격탐사 영상 약지도학습 인스턴스 분할 작업에 적합한 사전 정보 구동 체계를 구축하고 다중 사전 정보를 기반으로 하는 원격탐사 영상 약지도학습 인스턴스 분할 네트워크를 제안합니다. 구체적으로, 원격탐사 영상 약지도학습 인스턴스 분할 작업의 사전 정보는 출처에 따라 작업 사전과 영상 사전으로 구분되며, 작업 사전은 인스턴스 분할과 밀접한 관련이 있는 경계 상자 검출 작업에서 유래하고, 영상 사전은 영상 자체 정보의 귀납 및 발굴에서 비롯됩니다. 나아가, 작업 사전 정보를 구체화하는 3가지 구성 요소인 프레임-마스크 투영 일관성 제약, 픽셀 구분 난이도 표현 함수, 중심 위치 사전 제약을 설계하여 네트워크가 마스크 크기를 결정하고 영상 내 중요한 픽셀과 영역에 충분히 집중하도록 유도합니다; 영상 사전 정보를 구성하는 이웃 시각 일관성 제약과 그래디언트 일관성 제약 2가지 구성 요소를 설계하여 네트워크가 전경과 배경을 효율적으로 구분하고 원격탐사 영상의 복잡한 객체 윤곽선에 적응하도록 합니다. 광학 및 SAR 원격탐사 영상 데이터셋에 대한 실험 결과는 제안된 방법이 어떠한 픽셀 수준 주석 없이도 각각 52.5 및 54.1의 AP 값을 달성하며, 기존의 약지도 분할 방법보다 우수하고 완전 감독 Mask R-CNN의 89.3% 및 84.3%에 도달함을 보여줍니다. 이 방법은 원격탐사 영상의 세밀한 해석을 위한 고성능且저비용 솔루션을 제공합니다.

키워드

원격탐사 영상; 인스턴스 분할; 세밀 해석; 약지도학습; 사전 정보; 구동 체계; 객체 윤곽선; 주석 비용

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