EXTRACTION OF THEMES FROM AERIAL IMAGERY USING LATENT DIRICHLET ALLOCATION

被引：0
|
作者：
Deshpande, Shailesh [1 ]
Ladha, Shamsuddin [1 ]
Aggarwal, Hemant [2 ]
Yadav, Piyush [1 ]
机构：
[1] TATA Res Dev & Design Ctr, Pune, Maharashtra, India
[2] Indraprastha Inst Informat Technol, Delhi, India
来源：
2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2017年
关键词：
Thematic classification; Generative model; Latent Dirichlet Allocation; Urban planning;
D O I：
暂无
中图分类号：
P [天文学、地球科学];
学科分类号：
07 ;
摘要：
This paper presents the generative image processing model for extracting themes from aerial imagery. We model image as a collection of themes and each theme as a collection of objects. We learn urban themes using unsupervised Latent Dirichlet Allocation. Further, we use the learned topic (theme) model directly to infer some of the important parameters of facility management. On a test dataset of the port city of Zeebruges, our approach successfully identified open container parking lot, occupied parking lot, and open spaces in urban areas. Overall theme accuracy of our approach is about 83%.
引用
收藏
页码：4770 / 4773
页数：4
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