Chen J, Han Y, Wan L, et al. Geospatial relation captioning for high-spatial-resolution images by using an attention-based neural network[J]. International Journal of Remote Sensing, 2019, 40(16): 6482-6498.[DOI:10.1080/01431161.2019.1594439]
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Release time:2021-06-18 Abstract:High-spatial-resolution (HSR) remote sensing images serve as carriers of geographic information. Exploring geo-objects and their geospatial relations is fundamental in understanding HSR remote sensing images. To this end, this study proposes an intelligent semantic understanding method for HSR remote sensing images via geospatial relation captions. Firstly, we propose a method of geospatial relation expression to convey the topological, directional and distance relations of geo-objects in HSR images. Secondly, on the basis of images and their geospatial relation captions, an image dataset is constructed for model training. Finally, geospatial relation captioning is implemented for HSR images by using an attention-based deep neural network model. Experimental results demonstrate that the proposed captioning method can effectively provide geospatial semantics for HSR image understanding. Indexed by:Journal paper Translation or Not:no Included Journals:SCI Links to published journals:https://www.tandfonline.com/doi/full/10.1080/01431161.2019.1594439?needAccess=true -
Pre One:Chen J, Wan L, Zhu J, et al. Multi-scale spatial and channel-wise attention for improving object detection in remote sensing imagery[J]. IEEE Geoscience and Remote Sensing Letters, 2019, 17(4): 681-685.[DOI：10.1109/LGRS.2019.2930462]
Next One:Liu H, Yang M, Chen J, et al. Line-constrained shape feature for building change detection in VHR remote sensing imagery[J]. ISPRS International Journal of Geo-Information, 2018, 7(10): 410.[DOI：10.3390/ijgi7100410]
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