High spectral image classification method of autoencoder based on entropy rate superpixel segmentation

A technology of superpixel segmentation and hyperspectral image, which is applied in the field of hyperspectral image classification and autoencoder hyperspectral image classification, can solve the problem of low classification accuracy, achieve improved classification accuracy, improve hyperspectral classification performance, and better area consistent effect

Active Publication Date: 2018-01-16
XIDIAN UNIV
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[0005] The purpose of the present invention is to address the deficiencies of the above-mentioned existing methods, and propose a hyperspectral image classification method based on an auto

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  • High spectral image classification method of autoencoder based on entropy rate superpixel segmentation
  • High spectral image classification method of autoencoder based on entropy rate superpixel segmentation
  • High spectral image classification method of autoencoder based on entropy rate superpixel segmentation

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[0033] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0034] refer to figure 1 , a hyperspectral image classification method based on an autoencoder for entropy rate superpixel segmentation, including the following steps:

[0035] Step 1, input the hyperspectral image Indian Pines, see figure 2 (a), figure 2 (a) is Indian Pines hyperspectral three-dimensional image, is the data that experiment of the present invention uses, obtains training sample X from Indian Pines hyperspectral image p and test sample X q :

[0036] (1a) Convert the 3D hyperspectral image Indian Pines to a 2D hyperspectral image X a , X a ∈R 220×21025 , the image X a Contains 220 spectral bands, 21025 samples, and a total of 10249 samples with class labels from 1 to 16;

[0037] (1b) put X a Randomly select 10% of the samples with category labels from 1 to 16 to form the initial training sample set: X pp ...

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Abstract

The present invention provides a high spectral image classification method of an autoencoder based on entropy rate superpixel segmentation. The problem is mainly solved that a high spectral image classification performance is not good. The realization steps of the method comprise: obtaining a high spectral data training sample set and a test sample set; constructing n layers of autoencoder networks; inputting the training sample set, and employing a loss function to perform training of the autoencoder networks based on the entropy rate superpixel segmentation; and inputting the test data set,and employing the autoencoder networks based on the entropy rate superpixel segmentation after training to perform classification of a high spectral image. The high spectral image classification method of an autoencoder based on the entropy rate superpixel segmentation considers space context neighborhood information, excavates distribution features of data samples to effectively improve classification precision of a high spectral image, and can be applied to distinguishing and identification of surface features in fields of agricultural monitoring, geological prospecting, disaster environmentassessment and the like.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a hyperspectral image classification method, in particular to a hyperspectral image classification method based on entropy rate superpixel segmentation autoencoder, which is used for agriculture, surveying and mapping, archaeology, environment and disaster monitoring and other fields. Background technique [0002] With the development of science and technology, hyperspectral remote sensing technology has been greatly developed. Hyperspectral data can be represented as a hyperspectral data cube, which is a three-dimensional data structure. Hyperspectral data can be regarded as a three-dimensional image, and one-dimensional spectral information is added to the ordinary two-dimensional image. Its spatial image describes the two-dimensional spatial characteristics of the earth's surface, and its spectral dimension reveals the spectral curve characteristics of each pixel in t...

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IPC IPC(8): G06K9/62
Inventor 冯婕王琳刘立国焦李成张向荣张小华尚荣华刘红英
Owner XIDIAN UNIV
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