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Earth observation (EO) :
Understanding Earth’s surface via remote sensing technologies.
哪儿有什么、会怎么样
Earth Vision：Understanding Earth’s surface with images
多尺度、特殊边界条件的CV问题
Difficulties：less than 5% of EO Images were used
数据那么多，能看懂的没有几个
人工智能 + Earth Observation
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3
对地观测与识别年度进展之
Object Detection in Aerial Images
夏桂松
武汉大学
测绘遥感信息工程国家重点实验室
2018/4/24
Object Detection in Aerial Images (ODAI)
@JL-1 Satellite
@Digital Globe
@Google Earth
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Earth Observation
Earth observation (EO) is about understanding the planet Earth‘s
surface via remote sensing technologies.
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Challenges of ODAI
Large variations in the scale of objects
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Challenges of ODAI
Arbitrary orientation of instances
Densely packed & small instances
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What happened before 2016
Cheng-Han, A survey on object detection in optical remote sensing images. ISPRS J. Photo. & Remote Sensing, 20162018/4/24 8
2017: Data-driven & Deep Models for ODAI
Arbitrary orientation of instances :
• RICNN: [Li et. al., IEEE TGRS 2018]
• Faster-RCNN-OBB: [Xia et. al., CVPR’2018]
Small objects:
• Random Access Memories, [Zou et. al., IEEE TIP, 2018]
• Layout Proposal Network, [Hsieh et. al., ICCV’2017]
Benchmark dataset
• DOTA, [Xia et. al., CVPR’2018]
• DIUx xView,
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Arbitrary orientation of instances
• Rotation-Insensitive CNN [Li et. al., TGRS 2018]
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Arbitrary orientation of instances
• Rotation-Insensitive CNN [Li et. al., TGRS 2018]
multi-angle, multiscale and translation-invariant RPN Results
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Arbitrary orientation of instances
• Rotation covariant: Faster-RCNN-OBB: [Xia et. al., CVPR’2018]
RoiPooling
FCs
Output: quadrangle regressor
Softmax predict pP {( , ), 1,2,3,4}xi pyit t i
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Arbitrary orientation of instances
• Rotation covariant: Faster-RCNN-OBB: [Xia et. al., CVPR’2018]
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Sparsely distributed small objects
Random Access Memories [Zou et. al., IEEE TIP, 2018]
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Sparsely distributed small objects
Random Access Memories [Zou et. al., IEEE TIP, 2018]
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CNN + CFAR + Bayesian Estimation
Sparsely distributed small objects
Random Access Memories [Zou et. al., IEEE TIP, 2018]
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Layout Proposal Network: [Hsieh et. al., ICCV’2017]
Packed small objects
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Layout Proposal Network: [Hsieh et. al., ICCV’2017]
Packed small objects
Layout Proposal Networks
Using spatial kernel to encode contextual layout
information into detectors.
Idea: a predicted position with
more nearby cars can get higher
confidence and higher probability
to be localized as a car.
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Layout Proposal Network
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DOTA: A Large-scale Dataset for ODAI
About 200K instances (~0.5M now), 15 categories Annotated by oriented bounding boxes (OBBs) Large images reflecting scenarios in real applications
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[Xia et. al., CVPR’2018]
DOTA: A Large-scale Dataset for ODAI
mAP of the detection task with oriented bounding boxes
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DOTA: A Large-scale Dataset for ODAI
Xia, et. al. DOTA: A Large-scale Dataset for Object Detection in Aerial Images. CVPR’2018.
Link to DOTA Link to a contest on ICPR’2018
基金委空间信息网络重大研究计划-目标检测比赛（特等奖10万RMB）
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In 2018 : DIUx xView
DIUx xView 2018 Detection Challenge
OBJECTS IN CONTEXT IN OVERHEAD IMAGERY
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Lam et. al., xView:
Objects in Context in
Overhead Imagery,
arXiv:1802.07856,
2018,
xviewdataset.org
In 2018 : VisDrone2018
P. Zhu, L. Wen, X. Bian, H. Ling and Q. Hu, Vision Meets Drones: A Challenge.
http://www.aiskyeye.com
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References
• Cheng-Han, A survey on object detection in optical remote sensing images. ISPRS J.
Photo. & Remote Sensing, 2016.
• Li, et. al., Rotation-Insensitive and Context-Augmented Object Detection in Remote Sensing Images. IEEE TGRS, Vol. 56, No.4, pp.2337-2348, 2018.
• Xia, et. al. DOTA: A Large-scale Dataset for Object Detection in Aerial Images. CVPR’2018, https://captain-whu.github.io/DOTA
• Zou et. al., Random Access Memories: A New Paradigm for Target Detection in High Resolution Aerial Remote Sensing Images, IEEE TIP, Vol27, No.3, pp.1100-1111, 2018
• Hsieh et. al., Drone-based Object Counting by Spatially Regularized Regional Proposal
Network, ICCV’2017
• Lam et. al., xView: Objects in Context in Overhead Imagery, arXiv:1802.07856, 2018,http://xviewdataset.org
• P. Zhu, L. Wen, X. Bian, H. Ling and Q. Hu, Vision Meets Drones: A Challenge, arXiv2018, http://www.aiskyeye.com
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