Hyperspectral image classification method based on three-dimensional convolutional neural network

Abstract

The invention discloses a hyperspectral image classification method based on a three-dimensional convolutional neural network and relates to a hyperspectral image classification method. The inventionaims to solve a problem that an existing two-dimensional convolutional neural network coarsely rearranges a three-dimensional signal into a two-dimensional signal, spatial information in a hyperspectral image which can not be fully utilized, and the spatial information and spectral information in an original three-dimensional hyperspectral image are destroyed. The method comprises the steps of (1)inputting a hyperspectral image data set into a MATLAB platform and obtaining a processed data set, (2) taking a new hyperspectral image as a training set, (3) building the three-dimensional convolutional neural network according to the training set of a three-dimensional matrix form, (4) using the training set of the three-dimensional matrix form to train the three-dimensional convolutional neural network and obtaining a trained three-dimensional convolutional neural network, and (5) using a test set of a three-dimensional matrix form to input the trained three-dimensional convolutional neural network and obtaining a test set classification result. The method is used in the field of image classification.

Description

technical field [0001] The invention relates to a hyperspectral image classification method. Background technique [0002] The development direction of hyperspectral image classification technology is very rich, and the convolutional neural network has been found to be very suitable for the classification of hyperspectral images in recent years. However, the traditional convolutional neural network used for hyperspectral classification mostly uses two-dimensional Convolutional neural networks are used to classify hyperspectral images with distinct three-dimensional properties. This method will roughly rearrange the 3D signal into a 2D signal, which not only fails to make full use of the spatial information in the hyperspectral image, but also destroys the spatial and spectral information in the original 3D hyperspectral image. Contents of the invention [0003] The purpose of the present invention is to solve the rough rearrangement of three-dimensional signals into two-d...

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Problems solved by technology

[0003] The purpose of the present invention is to solve the rough rearrangement of three-dimensional signals into two-dimensional signals by the existing two-dimensional convolutional neural network, which not only cannot make full use of the spatial information in the hyperspectral image, but also destroys the original three-dimensional hyperspectral image. The problem of spatial information and spectral information, and propose a hyperspectral image classification method based on three-dimensional convolutional neural network

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