Orientation guided anchoring for geospatial object detection from remote sensing imagery

Object detection from remote sensing imagery plays a significant role in a wide range of applications, including urban planning, intelligent transportation systems, ecology and environment analysis, etc. However, scale variations, orientation variations, illumination changes, and partial occlusions,...

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Veröffentlicht in:ISPRS journal of photogrammetry and remote sensing Jg. 160; S. 67 - 82
Hauptverfasser: Yu, Yongtao, Guan, Haiyan, Li, Dilong, Gu, Tiannan, Tang, E., Li, Aixia
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.02.2020
Schlagworte:
cloud computing
comparative study
Convolutional neural network
data collection
ecology
lighting
neural networks
Object detection
Orientation guided anchoring
Oriented anchor
quantitative analysis
Region proposal network
remote sensing
Remote sensing imagery
transportation
urban planning
Oriented anchor
Region proposal network
Convolutional neural network
Remote sensing imagery
Object detection
Orientation guided anchoring
ISSN:0924-2716, 1872-8235
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Zusammenfassung:Object detection from remote sensing imagery plays a significant role in a wide range of applications, including urban planning, intelligent transportation systems, ecology and environment analysis, etc. However, scale variations, orientation variations, illumination changes, and partial occlusions, as well as image qualities, bring great challenges for accurate geospatial object detection. In this paper, we propose an efficient orientation guided anchoring based geospatial object detection network based on convolutional neural networks. To handle objects of varying sizes, the feature extraction subnetwork extracts a pyramid of semantically strong features at different scales. Based on orientation guided anchoring, the anchor generation subnetwork generates a small set of high-quality, oriented anchors as object proposals. After orientation region of interest pooling, objects of interest are detected from the object proposals through the object detection subnetwork. The proposed method has been tested on a large geospatial object detection dataset. Quantitative evaluations show that an overall completeness, correctness, quality, and F1-measure of 0.9232, 0.9648, 0.8931, and 0.9435, respectively, are obtained. In addition, the proposed method achieves a processing speed of 8 images per second on a GPU on the cloud computing platform. Comparative studies with the existing object detection methods also demonstrate the advantageous detection accuracy and computational efficiency of our proposed method.
Bibliographie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:0924-2716
1872-8235
DOI:10.1016/j.isprsjprs.2019.12.001