Journal
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
Volume 32, Issue 2, Pages 867-879Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCSVT.2021.3061265
Keywords
Feature extraction; Satellites; Drones; Buildings; Task analysis; Location awareness; Visualization; Geo-localization; image retrieval; agriculture; deep learning
Categories
Funding
- Zhejiang Lab's International Talent Fund for Young Professionals [ZJ2020GZ021]
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The paper introduces a deep neural network called LPN that leverages contextual information from neighbor areas to enrich discriminative clues for geo-localization. LPN adopts a square-ring feature partition strategy that provides attention based on distance to the image center, simplifying part matching and enabling part-wise representation learning.
Cross-view geo-localization is to spot images of the same geographic target from different platforms, e.g., drone-view cameras and satellites. It is challenging in the large visual appearance changes caused by extreme viewpoint variations. Existing methods usually concentrate on mining the fine-grained feature of the geographic target in the image center, but underestimate the contextual information in neighbor areas. In this work, we argue that neighbor areas can be leveraged as auxiliary information, enriching discriminative clues for geo-localization. Specifically, we introduce a simple and effective deep neural network, called Local Pattern Network (LPN), to take advantage of contextual information in an end-to-end manner. Without using extra part estimators, LPN adopts a square-ring feature partition strategy, which provides the attention according to the distance to the image center. It eases the part matching and enables the part-wise representation learning. Owing to the square-ring partition design, the proposed LPN has good scalability to rotation variations and achieves competitive results on three prevailing benchmarks, i.e., University-1652, CVUSA and CVACT. Besides, we also show the proposed LPN can be easily embedded into other frameworks to further boost performance.
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