Please use this identifier to cite or link to this item:
https://hdl.handle.net/10356/149071
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, Gongjie | en_US |
dc.contributor.author | Lu, Shijian | en_US |
dc.contributor.author | Zhang, Wei | en_US |
dc.date.accessioned | 2021-05-28T05:43:02Z | - |
dc.date.available | 2021-05-28T05:43:02Z | - |
dc.date.issued | 2019 | - |
dc.identifier.citation | Zhang, G., Lu, S. & Zhang, W. (2019). CAD-Net : a context-aware detection network for objects in remote sensing imagery. IEEE Transactions On Geoscience and Remote Sensing, 57(12), 10015-10024. https://dx.doi.org/10.1109/TGRS.2019.2930982 | en_US |
dc.identifier.issn | 0196-2892 | en_US |
dc.identifier.uri | https://hdl.handle.net/10356/149071 | - |
dc.description.abstract | Accurate and robust detection of multi-class objects in optical remote sensing images is essential to many real-world applications such as urban planning, traffic control, searching and rescuing, etc. However, state-of-the-art object detection techniques designed for images captured using ground-level sensors usually experience a sharp performance drop when directly applied to remote sensing images, largely due to the object appearance differences in remote sensing images in term of sparse texture, low contrast, arbitrary orientations, large scale variations, etc. This paper presents a novel object detection network (CAD-Net) that exploits attention-modulated features as well as global and local contexts to address the new challenges in detecting objects from remote sensing images. The proposed CAD-Net learns global and local contexts of objects by capturing their correlations with the global scene (at scene-level) and the local neighboring objects or features (at object-level), respectively. In addition, it designs a spatial-and-scale-aware attention module that guides the network to focus on more informative regions and features as well as more appropriate feature scales. Experiments over two publicly available object detection datasets for remote sensing images demonstrate that the proposed CAD-Net achieves superior detection performance. The implementation codes will be made publicly available for facilitating future researches. | en_US |
dc.description.sponsorship | Nanyang Technological University | en_US |
dc.language.iso | en | en_US |
dc.relation.ispartof | IEEE Transactions on Geoscience and Remote Sensing | en_US |
dc.rights | © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TGRS.2019.2930982. | en_US |
dc.subject | Engineering::Computer science and engineering | en_US |
dc.title | CAD-Net : a context-aware detection network for objects in remote sensing imagery | en_US |
dc.type | Journal Article | en |
dc.contributor.school | School of Computer Science and Engineering | en_US |
dc.identifier.doi | 10.1109/TGRS.2019.2930982 | - |
dc.description.version | Accepted version | en_US |
dc.identifier.arxiv | 1903.00857 | - |
dc.identifier.issue | 12 | en_US |
dc.identifier.volume | 57 | en_US |
dc.identifier.spage | 10015 | en_US |
dc.identifier.epage | 10024 | en_US |
dc.subject.keywords | Optical Remote Sensing Images | en_US |
dc.subject.keywords | Object Detection | en_US |
dc.subject.keywords | Deep Learning | en_US |
dc.subject.keywords | Convolutional Neural Networks (CNNs) | en_US |
dc.description.acknowledgement | This work was supported in part by the Nanyang Technological University under Start-Up Grant, in part by the National Key Research and Development Plan of China under Grant 2017YFB1300205, in part by the National Natural Science Foundation of China (NSFC) under Grant 61573222, and in part by the Major Research Program of Shandong Province under Grant 2018CXGC1503. | en_US |
item.fulltext | With Fulltext | - |
item.grantfulltext | open | - |
Appears in Collections: | SCSE Journal Articles |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
cad-net.pdf | 5.08 MB | Adobe PDF | View/Open |
SCOPUSTM
Citations
1
320
Updated on Mar 22, 2024
Web of ScienceTM
Citations
5
154
Updated on Oct 26, 2023
Page view(s)
305
Updated on Mar 27, 2024
Download(s) 20
320
Updated on Mar 27, 2024
Google ScholarTM
Check
Altmetric
Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.