Multi-group image classification method based on two-dimensional empirical modal decomposition and wavelet denoising

A technique of empirical mode decomposition and wavelet noise reduction, applied in the field of image processing, can solve the problems of low classification accuracy and insufficient utilization of image essential features, and achieve the effect of removing noise interference, facilitating popularization and application, and achieving good consistency.

Active Publication Date: 2010-09-29
哈尔滨工大正元信息技术有限公司
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Problems solved by technology

[0004] The purpose of the present invention is to solve the problems of insufficient utilization of image essential features and low classification accuracy by traditional classification methods, and provide a multi-group image classification method based on two-dimensional empirical mode decomposition and wavelet noise reduction

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  • Multi-group image classification method based on two-dimensional empirical modal decomposition and wavelet denoising
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  • Multi-group image classification method based on two-dimensional empirical modal decomposition and wavelet denoising

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specific Embodiment approach 1

[0016] Specific implementation mode one: the following combination figure 1 and figure 2 To describe this embodiment,

[0017] In 1998, Dr. Huang E of NASA (National Aeronautics and Space Administration, NASA) proposed Hilbert-Huang Transform (HHT) according to the mathematical theory design of modern mathematician Hilbert. As a powerful tool for analyzing nonlinear and non-stationary signals, HHT is divided into two steps. First use Empirical Mode Decomposition (EMD) to obtain a limited number of Intrinsic Mode Functions (IMF), and then use Hilbert Transform (HilbertTransform, HT) and instantaneous frequency method to obtain the instantaneous frequency and amplitude of IMF, Finally, the time-spectrum of the signal is obtained.

[0018] The core of HHT is EMD. EMD is completely driven by data, and its essence is to screen the signal according to the characteristic time scale of the signal. This process is represented by a scale band-pass filter to filter the signal, so th...

specific Embodiment approach 2

[0073] Specific implementation mode two: the following combination Figure 3 to Figure 14 This embodiment is described, and a specific example is given, using multi-group images of 92AV3C hyperspectral images.

[0074] Hyperspectral images are typical multi-group images. The selected 92AV3C hyperspectral image comes from the remote sensing observation of an agricultural area in northwestern Indiana, USA collected by the AVIRIS (Airborne Visible / Infrared Imaging Spectrometer) sensor. The image set contains 224 continuous waves, from 0.40 μm to 2.45 μm approximately every 10nm band. Remove 4 zero-value bands and 20 bands affected by water vapor absorption, and there are 200 bands available for actual experiments. The ground objects with the largest number of 7 types of pixels (ie corn-notill, corn-mintill, grass / trees, soybeans-notill, soybean-mintill, soybean-cleantill and woods) were selected as experimental samples. The total number of pixels of these 7 types of ground obje...

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Abstract

The invention relates to a multi-group image classification method based on two-dimensional empirical modal decomposition and wavelet denoising, belonging to the filed of image processing. The invention aims at solving the problems of insufficient utilization of image essential characteristics and low classification precision of the traditional classification method. The method comprises the following steps of: firstly, respectively carrying out two-dimensional empirical modal decomposition on each wave band in multi-group images to obtain the former K two-dimensional components and one residual error; secondly, summarizing the former K two-dimensional components as a characteristic value, and obtaining a denoised characteristic value after wavelet denoising; thirdly, randomly and proportionally selecting the denoised characteristic values of a plurality of multi-group images as training samples and test samples of a support vector machine, carrying out parameter training of the support vector machine on the training samples, and then carrying out attribution judgment to form a plurality of sub-classifiers of the support vector machine; and fourthly, constructing multiple classifiers based on a one-to-one strategy by utilizing the sub-classifiers of the support vector machine, and determining the attribution classes of the test samples according to a strategy function to complete the classification of the multi-group images.

Description

technical field [0001] The invention relates to a multi-group image classification method based on two-dimensional empirical mode decomposition and wavelet noise reduction, and belongs to the field of image processing. Background technique [0002] Multi-group images are a group of multi-band images with high correlation. There are a large number of physical prototypes in the fields of earth observation, medical diagnosis, and radar detection, such as hyperspectral images, medical ultrasound images, and sea level fluctuation images. They are generally continuous observations or multi-spectral spectroscopic observations of the same area, and often contain images of hundreds or thousands of bands, and the images of each band generally have a high correlation. Distinguish information, and there is a large amount of redundant information. [0003] Multi-group image classification can obtain more internal rules of the image, which is convenient for subsequent image processing, s...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62
Inventor 沈毅贺智张淼
Owner 哈尔滨工大正元信息技术有限公司
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