Deep multi-feature active migration network-based hyperspectral image classification method

Abstract

The invention provides a deep multi-feature active migration network-based hyperspectral image classification method. The method is used for solving the technical problem that the classification precision is low in the prior art. The method comprises the following steps of acquiring to-be-classified source domain and target domain hyperspectral images; preprocessing to-be-classified source domainhyperspectral images; acquiring a source domain marking sample set, a training sample set, a candidate sample set and a test sample set; constructing a source domain depth spectral feature extractionnetwork, and extracting the depth spectral features of the source domain marking sample set; constructing a source domain depth spatial feature extraction network, and extracting the depth space features of the source domain marking sample set; constructing a source domain depth spectrum-spatial joint feature extraction and classification network; and classifying to-be-classified target domain hyperspectral images by adopting a source domain depth multi-feature active migration network. According to the hyperspectral image high-precision classification method provided in the invention, the high-precision classification for different areas of one hyperspectral image or two hyperspectral images shot at the same place and at different time moments can be realized by utilizing a small number of training samples.

Description

technical field [0001] The invention belongs to the field of computer vision and pattern recognition, and relates to a hyperspectral image classification method, in particular to a hyperspectral image classification method based on a deep multi-feature active migration network, which can be used in fields such as agriculture, urban remote sensing, and environmental monitoring. Background technique [0002] A hyperspectral image is a three-dimensional image whose data can be expressed as a three-dimensional data structure composed of two-dimensional spatial data and one-dimensional spectral data. This type of image contains rich spectral information, including hundreds of continuous spectra from visible light to near-infrared. Moreover, hyperspectral images also provide rich spatial information, which has the characteristics of "integration of maps and spectra". Hyperspectral images effectively integrate the spectral and spatial information of remote sensing images, and can d...

AI Technical Summary

Problems solved by technology

However, the disadvantage of this method is that there is a large complexity in determining the appropriate kernel function and solving the kernel function, and the classification accuracy is low

This method makes full use of the sample points containing rich information, and the classification accuracy has been improved to a certain extent, but the defect is that this method can only realize the classification of a single hyperspectral image, and only the hyperspectral image is considered in the classification process The overall characteristics of the hyperspectral image, without considering the spatial context information and sample distribution of the hyperspectral image, affect the improvement of the classification accuracy

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