Author(s)

Yulong Zhao ¹, Min Hu ¹

Affiliation(s)

¹ Space Engineering University, Beijing, 101416, China

Corresponding Author

Yulong Zhao

ABSTRACT

The dimension reduction method of multi-source remote sensing image based on spatial information is proposed because of the influence of shadow on band selection and feature extraction. According to the separability of feature parameters, candidate feature parameters are selected to determine the classification threshold between samples. The feature parameters with separability greater than 6 are selected as the standard basis to construct the feature space of multi-source remote sensing image, so as to collect the feature space information of multi-source remote sensing image. According to the process of shadow detection algorithm, the original shadow image is transformed into HSV space to separate the dark area, and the dark area is denoised to get the shadow area. After shadow preprocessing, removing and post-processing, the boundary line is weakened and the undistorted information is obtained. According to the data structure of the multi-source remote sensing image, the image of each band in the multi-source remote sensing image can be expanded into one-dimensional vector by using the collected spatial information, and the independent components can be obtained by further solving. The independent component analysis (ICA) mathematical model was constructed to centralize the original mixed data and eliminate the mean value. The multi-source remote sensing image is whitened to form a new low-dimensional image. The experimental results show that the method is effective in dimensionality reduction.

KEYWORDS

Spatial information; Multi-source remote sensing image; Feature dimensionality reduction; Shadow detection and removal

CITE THIS PAPER

Yulong Zhao, Min Hu, Feature dimension reduction method of multi-source remote sensing image based on spatial information. Transactions on Aeronautics and Astronautics (2020) 1: 1-14. DOI: http://dx.doi.org/10.23977/tranaa.2020.010101.

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