고해상도 위성영상과 Fully Convolutional Network를 활용한 산림재해 피해지 탐지
활용도 Analysis
논문 Analysis
연구자 Analysis
저자
박성욱
김형우
이수진
윤예슬
김은숙
임종환
이양원
제어번호
106012531
학술지명
한국사진지리학회지
권호사항
Vol.
28
No.
4
[
2018
]
발행처
한국사진지리학회
자료유형
학술저널
수록면
87-101
(
15쪽)
언어
Korean
출판년도
2018
등재정보
KCI등재
판매처
'
고해상도 위성영상과 Fully Convolutional Network를 활용한 산림재해 피해지 탐지' 의 참고문헌
U-Net: Convolutional networks for biomedical image segmentation
The temporal dimension of differenced Normalized Burn Ratio (dNBR) fire/burn severity studies: the case of the large 2007 Peloponnese wildfires in Greece
Scene classification via a gradient boosting random convolutional network framework
Image-based atmospheric corrections- Revisited and improved
Fully Convolutional Networks for Semantic Segmentation
Deep learning classification of land cover and crop types using remote sensing data
Classification and segmentation of satellite orthoimagery using convolutional neural networks
Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA
Backpropagation applied to handwritten zip code recognition
Assessment of different spectral indices in the red-near-infrared spectral domain for burned land discrimination
A new metric for quantifying burn severity: the relativized burn ratio
'
고해상도 위성영상과 Fully Convolutional Network를 활용한 산림재해 피해지 탐지'
의 유사주제(
