Affiliations: [a] School of Tourism and Cultural Industry, Hunan University of Science and Engineering, Yongzhou, Hunan, China | [b] School of Information Engineering, Hunan University of Science and Engineering, Yongzhou, Hunan, China | [c] School of Architecture and Urban planning, Nanjing University, Nanjing, Jiangsu, China
Correspondence:
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Corresponding author: Huijun Xiao, School of Tourism and Cultural Industry, Hunan University of Science and Engineering, Yongzhou, Hunan 425199, China. E-mail: hnhjxiao2022@126.com.
Abstract: The feature extraction of Gaofen-2 Remote Sensing Image (RSI) has problems such as poor extraction accuracy and large noise reduction error. Therefore, this paper designs an RSI feature extraction method based on high score 2 wavelet transform (WT). The RSI is collected with the help of Gaofen-2 satellite and high-resolution remote sensing technology, the key points of the image are determined through the Gaussian difference scale space, and the key points of the edge are judged by the peak curvature value of the difference function at the edge junction, so as to complete the RSI acquisition. Specific filtering and spatial domain transformations are used to remove image noise and improve the quality of RSI. The mean shift (MS) algorithm is used to iteratively find the area with the most dense sample points in the RSI space, complete the image analysis, and realize the preprocessing of the high score 2 RSI. The linear features of the RSI are determined by the WT algorithm, and the image threshold is set for feature extraction of the high score 2 RSI. The experimental results show that in the RSI noise reduction error analysis of different methods, the noise reduction error curve of the sample RSI of the method proposed in this paper has the lowest trend, which is always lower than 2%. Compared with the two methods proposed before, the error is higher. At the same time, in the accuracy analysis of key point feature extraction, the proposed scheme has better accuracy. Therefore, it can be seen that this method has better comprehensive performance, and the proposed method can effectively improve the feature extraction accuracy of RSI and reduce the noise in RSI.
Keywords: WT, high score 2 RSI, feature extraction, peak value of difference function, mass spectrometry algorithm
