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]
发布时间：2021-06-18
点击次数：
摘要：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.
论文类型：期刊论文
是否译文：否
收录刊物：SCI
发布期刊链接：https://www.tandfonline.com/doi/full/10.1080/01431161.2019.1594439?needAccess=true
上一条： 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]
下一条： 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]