Hyperspectral image classification-oriented data adaptive activation function learning method

Abstract

The invention discloses a hyperspectral image classification-oriented data adaptive activation function learning method, which is used for solving the technical problem of poor accuracy of an existinghyperspectral image classification method. According to the technical scheme, spectral information and spatial information of hyperspectral data are utilized, and hyperspectral image classification is carried out by learning an activation function based on specific data. By analyzing a common activation function, it is found that the activation function can be represented in the mode that activation mapping is multiplied by input features. Therefore, activation mapping is learned by constructing a two-layer neural network. The neural network learns from two aspects of channels and spectrums,and activation functions conforming to the characteristics of the channels and the spectrums are constructed respectively, so that the image classification accuracy is improved. Tests show that on anIndian Pines data set, the classification accuracy of the activation function based on the channel and the spectrum is averagely improved by 2.17% and 4.03% respectively.

Description

technical field [0001] The invention relates to a hyperspectral image classification method, in particular to a data adaptive activation function learning method for hyperspectral image classification. Background technique [0002] Hyperspectral image (Hyperspectral Image, HSI) contains both detailed spatial information and rich spectral information, and is widely used in remote sensing fields such as terrain classification, environmental detection and geological survey. Hyperspectral image classification is an important task in hyperspectral image analysis. The purpose is to assign a defined label to each pixel, which can be simply divided into traditional machine learning methods and deep learning methods. Traditional machine learning methods include Support Vector Machine (SVM), Multinomial Logistic Regression (MLR), etc. Although these methods are simple and effective, their shallow structure prevents them from learning enough in challenging situations. useful features....

AI Technical Summary

Problems solved by technology

[0004] In order to overcome the deficiency of the poor accuracy rate of existing hyperspectral image classification methods, the present invention provides a data adaptive activation function learning method for hyperspectral image classification

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