Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/149071
Title: CAD-Net : a context-aware detection network for objects in remote sensing imagery
Authors: Zhang, Gongjie
Lu, Shijian
Zhang, Wei
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: 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
Journal: IEEE Transactions on Geoscience and Remote Sensing
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.
URI: https://hdl.handle.net/10356/149071
ISSN: 0196-2892
DOI: 10.1109/TGRS.2019.2930982
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.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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