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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| Vydáno v: | ISPRS journal of photogrammetry and remote sensing Ročník 160; s. 67 - 82 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
01.02.2020
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| Témata: | |
| ISSN: | 0924-2716, 1872-8235 |
| On-line přístup: | Získat plný text |
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| Abstract | 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. |
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| AbstractList | 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. 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. |
| Author | Li, Aixia Guan, Haiyan Li, Dilong Yu, Yongtao Gu, Tiannan Tang, E. |
| Author_xml | – sequence: 1 givenname: Yongtao orcidid: 0000-0001-7204-9346 surname: Yu fullname: Yu, Yongtao email: allennessy@hyit.edu.cn organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China – sequence: 2 givenname: Haiyan orcidid: 0000-0003-3691-8721 surname: Guan fullname: Guan, Haiyan email: guanhy.nj@nuist.edu.cn organization: School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, JS 210044, China – sequence: 3 givenname: Dilong surname: Li fullname: Li, Dilong organization: State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan, HB 430072, China – sequence: 4 givenname: Tiannan surname: Gu fullname: Gu, Tiannan organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China – sequence: 5 givenname: E. surname: Tang fullname: Tang, E. organization: Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, JS 223003, China – sequence: 6 givenname: Aixia surname: Li fullname: Li, Aixia organization: College of Surveying & Municipal Engineering, Zhejiang University of Water Resources and Electric Power, Hangzhou, ZJ 310018, China |
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| Keywords | Oriented anchor Region proposal network Convolutional neural network Remote sensing imagery Object detection Orientation guided anchoring |
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52 Peng (10.1016/j.isprsjprs.2019.12.001_b0215) 2008; 1 Ren (10.1016/j.isprsjprs.2019.12.001_b0235) 2017; 39 He (10.1016/j.isprsjprs.2019.12.001_b0110) 2016 Hong (10.1016/j.isprsjprs.2019.12.001_b0115) 2019; 19 Zhang (10.1016/j.isprsjprs.2019.12.001_b0360) 2017; 14 Rishikeshan (10.1016/j.isprsjprs.2019.12.001_b0245) 2018; 146 Wagner (10.1016/j.isprsjprs.2019.12.001_b0260) 2018; 145 Li (10.1016/j.isprsjprs.2019.12.001_b0140) 2018; 56 Lin (10.1016/j.isprsjprs.2019.12.001_b0150) 2015; 12 Yokoya (10.1016/j.isprsjprs.2019.12.001_b0310) 2015; 8 10.1016/j.isprsjprs.2019.12.001_b0255 Zhang (10.1016/j.isprsjprs.2019.12.001_b0345) 2015; 53 Zhang (10.1016/j.isprsjprs.2019.12.001_b0350) 2018; 15 Perrin (10.1016/j.isprsjprs.2019.12.001_b0220) 2004 Cai (10.1016/j.isprsjprs.2019.12.001_b0035) 2018; 15 Craciun (10.1016/j.isprsjprs.2019.12.001_b0065) 2013 Ding (10.1016/j.isprsjprs.2019.12.001_b0075) 2018; 141 Lin (10.1016/j.isprsjprs.2019.12.001_b0160) 2017 Maboudi (10.1016/j.isprsjprs.2019.12.001_b0185) 2018; 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