The increasing availability and accessibility of numerous overhead images allows us to estimate and assess the spatial arrangement of groups of geospatial target objects, which can benefit many applications, such as traffic monitoring and agricultural monitoring. Spatial arrangement estimation is the process of identifying the areas which contain the desired objects in overhead images. Traditional supervised object detection approaches can estimate accurate spatial arrangement but require large amounts of bounding box annotations. Recent semi-supervised clustering approaches can reduce manual labeling but still require annotations for all object categories in the image. This paper presents the target-guided generative model (TGGM), under the Variational Auto-encoder (VAE) framework, which uses Gaussian Mixture Models (GMM) to estimate the distributions of both hidden and decoder variables in VAE. Modeling both hidden and decoder variables by GMM reduces the required manual annotations significantly for spatial arrangement estimation. Unlike existing approaches that the training process can only update the GMM as a whole in the optimization iterations (e.g., a minibatch), TGGM allows the update of individual GMM components separately in the same optimization iteration. Optimizing GMM components separately allows TGGM to exploit the semantic relationships in spatial data and requires only a few labels to initiate and guide the generative process. Our experiments shows that TGGM achieves results comparable to the state-of-the-art semi-supervised methods and outperformes unsupervised methods by 10% based on the F scores, while requiring significantly fewer labeled data.
|Original language||English (US)|
|Title of host publication||Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021|
|Editors||Yixin Chen, Heiko Ludwig, Yicheng Tu, Usama Fayyad, Xingquan Zhu, Xiaohua Tony Hu, Suren Byna, Xiong Liu, Jianping Zhang, Shirui Pan, Vagelis Papalexakis, Jianwu Wang, Alfredo Cuzzocrea, Carlos Ordonez|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|Number of pages||10|
|State||Published - 2021|
|Event||2021 IEEE International Conference on Big Data, Big Data 2021 - Virtual, Online, United States|
Duration: Dec 15 2021 → Dec 18 2021
|Name||Proceedings - 2021 IEEE International Conference on Big Data, Big Data 2021|
|Conference||2021 IEEE International Conference on Big Data, Big Data 2021|
|Period||12/15/21 → 12/18/21|
Bibliographical noteFunding Information:
VI. ACKNOWLEDGE This material is based upon work supported in part by the National Science Foundation under Grant Nos. IIS 1564164 (to the University of Southern California) and IIS 1563933 (to the University of Colorado at Boulder), NVIDIA Corporation, the National Endowment for the Humanities under Award No. HC-278125-21, and the University of Minnesota, Computer Science & Engineering Faculty startup funds.
© 2021 IEEE.