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Local similarity preserving-based hyperspectral image extreme learning machine clustering method

A hyperspectral image and extreme learning machine technology, applied in the field of hyperspectral image extreme learning machine clustering, can solve problems such as low clustering accuracy, no effective joint space-spectral information, and algorithm performance degradation.

Active Publication Date: 2018-06-22
NANJING UNIV OF SCI & TECH
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Problems solved by technology

However, the above method only utilizes the hyperspectral pixel-by-pixel spectral information, does not effectively combine spatial-spectral information, the clustering accuracy is low, and the performance of the algorithm decreases when there is noise in the data

Method used

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  • Local similarity preserving-based hyperspectral image extreme learning machine clustering method
  • Local similarity preserving-based hyperspectral image extreme learning machine clustering method
  • Local similarity preserving-based hyperspectral image extreme learning machine clustering method

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Embodiment

[0089] combine figure 1 , the implementation process of the present invention is described in detail below, and the steps are as follows:

[0090] Step 1: Reorganize the hyperspectral pixel matrix: input a hyperspectral image X 0 ∈ R D×W×H , taking the Selina_A data set with image band number D=204, image width W=86, and image height H=83 shown in Figure 4(a) as an experimental example.

[0091] The original hyperspectral data X 0 Arrange pixel by pixel to form matrix X=[x 1 ,x 2 ,...,x N ]∈R D×N As the input of the model, where N=W×H represents the number of hyperspectral pixels, x i ∈ R D Represents a hyperspectral pixel.

[0092] Step 2: Calculate the linear random response of hidden layer neurons: the specific process is as follows:

[0093] 2.1 Calculate the linear random response of the j-th hidden layer neuron to the i-th hyperspectral pixel:

[0094]

[0095] Among them, 1≤i≤N, w j =[w j1 ,w j2 ,...,w jD ]∈R D is a randomly generated weight vector, b...

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Abstract

The invention discloses a local similarity preserving-based hyperspectral image extreme learning machine clustering method. The method comprises the steps of organizing a hyperspectral pixel matrix; calculating a linear random response of a hidden layer neuron; calculating a nonlinear activation value of the hidden layer neuron; performing three-dimensional reconstruction of hidden layer feature data; performing spatial guided filtering; performing two-dimensional reconstruction of the filtered hidden layer feature data; building local similarity preserving regular terms and an optimization model; and calculating local similarity preserving projection features, and performing K-means clustering to obtain a final clustering tag. Based on a conventional extreme learning machine, hyperspectral image spatial information of local neighborhoods is integrated through the guided filtering, and spectral local similarity of a hyperspectrum is fully utilized; projection with local preservabilityis calculated through model optimization; spatial spectral joint information is extracted; the clustering precision is improved; the calculation complexity is lowered; and the method can be widely applied to the hyperspectral unsupervised classification in the fields of territorial resources, mineral survey and precision agriculture.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a hyperspectral image extreme learning machine clustering method with local similarity preservation. Background technique [0002] Due to its spectral correlation and rich spatial information, hyperspectral images are widely used in military monitoring, precision agriculture and mineral monitoring, among which hyperspectral image clustering is one of the most important research contents. The basic principle of hyperspectral image clustering is to perform unsupervised classification of target images on the basis of image clustering, combined with the spectral characteristics of hyperspectral images. The theoretical basis is that the same pixel has the same or similar spectral characteristics, and conversely, different pixels correspond to different spectral characteristics. [0003] At present, many clustering algorithms for hyperspectral image...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04
CPCG06N3/048G06F18/213G06F18/23213Y02A40/10
Inventor 肖亮徐金环
Owner NANJING UNIV OF SCI & TECH
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