TY - JOUR
T1 - Automated high-resolution earth observation image interpretation
T2 - Outcome of the 2020 gaofen challenge
AU - Sun, Xian
AU - Wang, Peijin
AU - Yan, Zhiyuan
AU - Diao, Wenhui
AU - Lu, Xiaonan
AU - Yang, Zhujun
AU - Zhang, Yidan
AU - Xiang, Deliang
AU - Yan, Chen
AU - Guo, Jie
AU - Dang, Bo
AU - Wei, Wei
AU - Xu, Feng
AU - Wang, Cheng
AU - Hansch, Ronny
AU - Weinmann, Martin
AU - Yokoya, Naoto
AU - Fu, Kun
N1 - Publisher Copyright:
© 2008-2012 IEEE.
PY - 2021
Y1 - 2021
N2 - In this article, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically cosponsored by the IEEE Geoscience and Remote Sensing Society and the International Society for Photogrammetry and Remote Sensing. It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep-learning-based methods have achieved good performance on image interpretation. In this article, we report the details and the best-performing methods presented so far in the scope of this challenge.
AB - In this article, we introduce the 2020 Gaofen Challenge and relevant scientific outcomes. The 2020 Gaofen Challenge is an international competition, which is organized by the China High-Resolution Earth Observation Conference Committee and the Aerospace Information Research Institute, Chinese Academy of Sciences and technically cosponsored by the IEEE Geoscience and Remote Sensing Society and the International Society for Photogrammetry and Remote Sensing. It aims at promoting the academic development of automated high-resolution earth observation image interpretation. Six independent tracks have been organized in this challenge, which cover the challenging problems in the field of object detection and semantic segmentation. With the development of convolutional neural networks, deep-learning-based methods have achieved good performance on image interpretation. In this article, we report the details and the best-performing methods presented so far in the scope of this challenge.
KW - Convolutional neural networks
KW - Gaofen Challenge
KW - object detection and recognition
KW - optical images
KW - SAR images
KW - semantic segmentation
UR - http://www.scopus.com/inward/record.url?scp=85114623215&partnerID=8YFLogxK
U2 - 10.1109/JSTARS.2021.3106941
DO - 10.1109/JSTARS.2021.3106941
M3 - 文章
AN - SCOPUS:85114623215
SN - 1939-1404
VL - 14
SP - 8922
EP - 8940
JO - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
JF - IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ER -