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Zhipeng Mao

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Proceedings Article | 14 December 2021 Paper

Isometric mapping transfer learning based on multi-parameter optimization for infrared water target extraction

Xin Wang, Zhipeng Mao, Mingjun Xu, Lin Duan

Proceedings Volume 12087, 120870A (2021) https://doi.org/10.1117/12.2624713

KEYWORDS: Remote sensing, Infrared radiation, Infrared imaging, Visible radiation, Infrared sensors, Detection and tracking algorithms, Feature extraction, Satellites, Satellite imaging, Associative arrays

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Compared with visible light remote sensing images, infrared remote sensing data has a lower gray level, and its visual interpretation requires high manual experience and consumes time and energy. In this paper, a transfer learning method is proposed to train the infrared remote sensing water body extraction model based on the label information of visible light remote sensing images and a small amount of infrared remote sensing image label information. Feature-based subspace transfer learning maps different types of remote sensing image features into a shared subspace and classifies them under similar distribution. According to the analysis of features of water bodies, the nonlinear transfer learning algorithm is used to reduce the dimensionality of features. The manifold feature extraction algorithm can retain the original nonlinear distance relationship of remote sensing data after dimensionality reduction so that the water target maintains the original nonlinear features in the manifold subspace, and accurate water extraction results can be obtained. Experiments are carried out on the Band5 NIR image data set from the Lansat8 satellite. The average accuracy of the proposed method is 95.44%, which is better than other methods. The results show that the proposed isometric mapping transfer learning method based on multi-parameter optimization can accurately extract infrared water targets.

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