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High-resolution multi-time-phase remote sensing image classification method based on multi-connection decision manifold alignment

A high-resolution, remote-sensing image technology, applied in the direction of instruments, character and pattern recognition, computer components, etc., can solve the problems of fewer spectral bands, low accuracy of multi-time alignment classification, spectral aliasing, etc., to expand the scope of application, Realize the effect of deepening utilization and improving ability

Active Publication Date: 2017-03-08
天岸马科技(黑龙江)有限公司
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  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the multi-temporal alignment classification under high-scoring conditions, that is, to solve the problem of low accuracy of traditional multi-temporal alignment classification caused by fewer spectral bands and serious spectral aliasing, and propose a multi-temporal alignment based on multi-connection decision manifold Quasi-high-resolution multi-temporal remote sensing image classification method

Method used

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  • High-resolution multi-time-phase remote sensing image classification method based on multi-connection decision manifold alignment
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  • High-resolution multi-time-phase remote sensing image classification method based on multi-connection decision manifold alignment

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specific Embodiment approach 1

[0029] Specific implementation mode one: combine figure 1 To illustrate this embodiment, the specific process of a high-resolution multi-temporal remote sensing image classification method based on multi-connection decision manifold alignment in this embodiment is as follows:

[0030] Step 1. Input the spectral matrices A and B of all spatial points in the source phase and the target phase, and the corresponding category label vector Y of each row in A;

[0031] Step 2. Calculate the similarity matrix W between A, B and each row in A and B respectively 1 , W 2 and W 12 ;

[0032] Step 3. Calculate W through multi-connection decision-making 1 , W 2 The corresponding source-time-phase single-connection Laplacian graph G 1 , target phase multi-connected Laplacian graph G 2 , and the source-phase and target-phase single-connection similarity matrix W 12 Multi-connection decision-making optimization;

[0033] Step 4, put A, B, G 1 , G 2 and W 12 Input into the independe...

specific Embodiment approach 2

[0035] Specific embodiment 2: The difference between this embodiment and specific embodiment 1 is: in the step 2, calculate the similarity matrix W between A, B and each row in A and B 1 , W 2 and W 12 ; The specific process is:

[0036]

[0037]

[0038]

[0039] In the formula, W respectively 1 , W 2 and W 12The elements in row i and column j of , σ is the control parameter of similarity; A i,: For all elements of the i-th row of the matrix A, A j,: For all elements of the jth row of the matrix A, B i,: For all elements of the i-th row of the matrix B, B j,: is all the elements of the jth row of the matrix B, and : is all the columns of the ith row.

[0040] Other steps and parameters are the same as those in Embodiment 1.

specific Embodiment approach 3

[0041] Specific implementation mode three: combination Figure 2a , Figure 2b , Figure 2c Describe this implementation mode. The difference between this implementation mode and the specific implementation mode 1 or 2 is that in the step 3, W is calculated by means of multi-connection decision-making. 1 , W 2 The corresponding source-time-phase single-connection Laplacian graph G 1 , target phase multi-connected Laplacian graph G 2 , and the source-phase and target-phase single-connection similarity matrix W 12 Multi-connection decision-making optimization; the specific process is:

[0042] Step 31, in turn for W 1 Each row of performs the following operations: find W 1 The first p values ​​sorted from large to small in the i-th row, and find the corresponding label of the position of the p value in the label vector Y, count the label category with the most occurrences among the p labels that appear, and then find The maximum position t of the similarity value of the ...

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Abstract

A high-resolution multi-time-phase remote sensing image classification method based on multi-connection decision manifold alignment is disclosed. The invention relates to the high-resolution multi-time-phase remote sensing image classification method and aims at solving a problem of multi-time-phase alignment classification under a high-resolution condition. The method comprises the following steps of 1, inputting spectrum matrixes A and B of all the space points in a source time phase and a target time phase and each row of corresponding type label vector Y in the A; 2, calculating the A, the B, W1, W2 and W12 respectively, wherein the W1, the W2 and the W12 among each row in the A and the B; 3, through a multi-connection decision mode, calculating G1 and G2 corresponding to the W1 and the W2, and multi-connection decision optimization of the W12; 4. Inputting the A, the B, the G1, the G2 and the W12 into an independence spectrum alignment model and acquiring mapping matrixes F1 and F2 of the A and the B in an alignment space; and 5, through a KNN classification model, carrying out classification and acquiring a classification label of the target time phase. The method is used for the image classification field.

Description

technical field [0001] The invention relates to a high-resolution multi-temporal remote sensing image classification method. Background technique [0002] Multispectral optical remote sensing is an important branch in the field of remote sensing. The current multispectral optical remote sensing technology is gradually developing from low spatial resolution to high spatial resolution, from typical MODIS data with a resolution of 1000 meters to Landsat with a spatial resolution of 30 meters. The data is in my country's Gaofen No. 1 data with a resolution of 8 meters and Gaofen No. 2 data with a resolution of 3.2 meters. High spatial resolution multi-spectral remote sensing images not only contain the spectral information of ordinary ground object images, but also contain the geometric structure and texture information of ground objects. The application of high-resolution remote sensing image technology not only improves the traditional analysis scale, but also expands the trad...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24147
Inventor 谷延锋高国明
Owner 天岸马科技(黑龙江)有限公司