Hyperspectral Image Classification Method Based on Ridgelet and Deep Convolutional Networks

Abstract

The invention discloses a hyperspectral image classification method based on ridgelet and deep convolution network, which mainly solves the problems of low classification accuracy and high computational complexity of hyperspectral image classification in the prior art. The implementation steps are: 1. Select training samples in the hyperspectral image; 2. Extract the spectral information and spatial information of the training samples; 3. Combine the spectral information and spatial information to form a training sample set; 4. Construct a five-layer deep convolutional network , and design a ridgelet filter to initialize the network; 5. Use the training sample set to train the constructed neural network; 6. Classify the remaining samples with the trained neural network to complete the image classification. The invention has the advantages of high classification accuracy and fast classification speed, and can be used in meteorological monitoring, environmental monitoring, urban planning and disaster prevention and reduction.

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 characteristic 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 hyperspectral...

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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