Enhancing few-shot UAV image classification with laser navigation-assisted decision fusion

doi:10.13474/j.cnki.11-2246.2024.0731

Abstract

Abstract: In recent years, the integration of laser navigation technology with few-shot UAV aerial image classification has provided unprecedented precise spatial positioning and valuable information for fields such as land use survey, urban planning, and environmental monitoring, significantly enhancing the importance of classification technology applications. This study proposes a method of few-shot UAV aerial image classification that integrates laser navigation and decision fusion techniques, aimed at improving classification performance and spatial positioning accuracy. By utilizing the high-precision geographic location information provided by the laser navigation system, the feature extraction process of aerial images is optimized. The study adopts self-supervised learning to construct auxiliary tasks, enhancing the generalization ability of feature extractors through rotation and flipping techniques. Moreover, combining feature extractors trained with two self-supervised paradigms and utilizing a logistic regression classifier for the classification task. A novel decision fusion module is designed to automatically adjust the weights of each decision, enhancing classification accuracy. Experimental results on the NWPU-RESISC45 and UC Merced datasets validate the effectiveness and advanced nature of the proposed method, demonstrate the potential of laser navigation technology in enhancing few-shot UAV aerial image classification.

Key words: few-shot, UAV aerial image classification, decision fusion, self-supervised learning, laser navigation

CLC Number:

P237

XIE Xingsheng, ZHANG Yongting, DING Zongbao, JIANG Yuhuan, LIU Jian. Enhancing few-shot UAV image classification with laser navigation-assisted decision fusion[J]. Bulletin of Surveying and Mapping, 2024, 0(7): 173-177.

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