TY - GEN
T1 - Recognizing terrain features on terrestrial surface using a deep learning model - An example with crater detection
AU - Li, WenWen
AU - Zhou, Bin
AU - Hsu, Chia Yu
AU - Li, Yixing
AU - Ren, Fengbo
N1 - Funding Information:
This work was partially supported by U.S. Geological Survey
Publisher Copyright:
© 2017 Association for Computing Machinery.
PY - 2017/11/7
Y1 - 2017/11/7
N2 - This paper exploits the use of a popular deep learning model - the faster-RCNN - to support automatic terrain feature detection and classification using a mixed set of optimal remote sensing and natural images. Crater detection is used as the case study in this research since this geomorphological feature provides important information about surface aging. Craters, such as impact craters, also effect global changes in many aspects, such as geography, topography, mineral and hydrocarbon production, etc. The collected data were labeled and the network was trained through a GPU server. Experimental results show that the faster-RCNN model coupled with a widely used convolutional network ZF-net performs well in detecting craters on the terrestrial surface.
AB - This paper exploits the use of a popular deep learning model - the faster-RCNN - to support automatic terrain feature detection and classification using a mixed set of optimal remote sensing and natural images. Crater detection is used as the case study in this research since this geomorphological feature provides important information about surface aging. Craters, such as impact craters, also effect global changes in many aspects, such as geography, topography, mineral and hydrocarbon production, etc. The collected data were labeled and the network was trained through a GPU server. Experimental results show that the faster-RCNN model coupled with a widely used convolutional network ZF-net performs well in detecting craters on the terrestrial surface.
KW - Crater
KW - Deep learning
KW - Region proposal network
KW - Terrain feature recognition
UR - http://www.scopus.com/inward/record.url?scp=85040218412&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85040218412&partnerID=8YFLogxK
U2 - 10.1145/3149808.3149814
DO - 10.1145/3149808.3149814
M3 - Conference contribution
AN - SCOPUS:85040218412
T3 - Proceedings of the 1st Workshop on GeoAI: AI and Deep Learning for Geographic Knowledge Discovery, GeoAI 2017
SP - 33
EP - 36
BT - Proceedings of the 1st Workshop on GeoAI
PB - Association for Computing Machinery, Inc
T2 - 1st Workshop on GeoAI: AI and Deep Learning for Geographic Knowledge Discovery, GeoAI 2017
Y2 - 7 November 2017 through 10 November 2017
ER -