Hyper-spectral image classification method based on ridgelet and depth convolution network

Abstract

The invention discloses a hyper-spectral image classification method based on ridgelet and a depth convolution network, and mainly aims to solve the problem that the accuracy is low and the computational complexity is high for hyper-spectral image classification through the existing technology. The method comprises the following steps: (1) selecting training samples in a hyper-spectral image; (2) extracting spectral information and spatial information of the training samples; (3) forming training sample sets based on the spectral information and spatial information; (4) constructing a five-layer depth convolution network, and designing a ridgelet filter to initialize the network; (5) using the training sample sets to train a constructed neural network; and (6) using the trained neural network to classify the other training samples, thus completing image classification. The method has the advantages of high classification accuracy and high classification speed, and can be used in weather monitoring, environmental monitoring, urban planning, and disaster prevention and mitigation.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a hyperspectral image classification method, which can be used for meteorological monitoring, environmental monitoring, land utilization, urban planning, and disaster prevention and mitigation. Background technique [0002] Hyperspectral resolution remote sensing refers to the use of many narrow electromagnetic wave bands to obtain relevant data from objects of interest. Its biggest feature is that while obtaining the two-dimensional space scene information of the target image, it can also obtain high-resolution one-dimensional spectral information representing its physical properties, that is, it has the characteristics of "integration of maps and spectra", which represents the latest achievements of remote sensing. One of the new technologies. The main difference between hyperspectral remote sensing and conventional remote sensing data is that hyperspectra...

AI Technical Summary

Problems solved by technology

[0010] The purpose of the present invention is to address the deficiencies of the above-mentioned prior art, and propose a hyperspectral image classification method based on ridgelet and deep convolutional network, so as to solve the difficulty in learning effective classification features in the hyperspectral image classification problem of the prior art , and the problem that traditional deep convolutional networks are difficult to achieve smaller computational complexity, improving the accuracy and speed of spectral image classification

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