Semi-supervised hyperspectral remote sensing image classification method based on information entropies

A technology for hyperspectral remote sensing and image classification, applied in the field of hyperspectral remote sensing images, semi-supervised hyperspectral remote sensing image classification based on information entropy, which can solve the problem of insufficient use of unlabeled labels.

Inactive Publication Date: 2015-02-25
HENAN POLYTECHNIC UNIV
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Problems solved by technology

[0007] In view of the deficiencies in the prior art, the purpose of the present invention is to provide a semi-supervised hyperspectral remote sensing image classification method based on informati

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  • Semi-supervised hyperspectral remote sensing image classification method based on information entropies
  • Semi-supervised hyperspectral remote sensing image classification method based on information entropies
  • Semi-supervised hyperspectral remote sensing image classification method based on information entropies

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Embodiment 1

[0048] Embodiment 1: The semi-supervised hyperspectral remote sensing image classification method based on information entropy is the same as the steps of the above-mentioned specific implementation; figure 2 (a) is the hyperspectral remote sensing original image that the present invention uses, and it is the image on March 23rd, 1996 of Florida Kennedy Space Center (KSC) that the airborne imaging spectrometer AVIRIS of National Aeronautics and Space Administration (NASA) obtains, There are a total of 224 bands, the spectral range is 400-2500, the spectral resolution is 10nm, and the spatial resolution is 18m. For the data in the study area, the effects of water vapor absorption and low SNR bands are removed, and a total of 120 bands are selected for analysis. The training data is selected according to the images provided by the Landsat Thematic Mapper (Landsat Thematic Mapper). According to the interpretation of the images, the land cover in this area is divided into 13 major...

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Abstract

The invention discloses a semi-supervised hyperspectral remote sensing image classification method based on information entropies and relates to the field of remote sensing. The method specifically comprises the steps of (1) inputting a hyperspectral remote sensing image, (2) inputting a training sample set, (3) inputting a category set corresponding to training samples, (4) calculating the probability of the category which each image element in the hyperspectral remote sensing image represents through a multi-classification linear regression method, (5) outputting the categories corresponding to all the image elements according to the calculated probabilities of all the image elements, (6) outputting the classification result and judging the accuracy of the output result, (7) converting the probabilities of all the image elements in the remote sensing image into indeterminacy through the Renyi entropy algorithm, (8) converting unmarked label image elements in the hyperspectral remote sensing image into marked label image elements according to the indeterminacy, (9) adding new marked labels into the training set, and (10) conducting iteration operation. The hyperspectral remote sensing image classification method has the advantages of being easy to realize, low in calculation complexity and the like.

Description

technical field [0001] The invention relates to the technical field of remote sensing image processing, and is especially suitable for hyperspectral remote sensing images. It proposes a semi-supervised hyperspectral remote sensing image classification method based on information entropy, which can be applied to hyperspectral remote sensing image classification and recognition and land use / coverage classification and many other applications. Background technique [0002] The research on hyperspectral remote sensing image classification method is one of the core and key research fields of hyperspectral image data processing method. Its general goal is to divide all pixels in remote sensing image The spatial distribution of classes and the details of various ground objects have been widely used in both military and civilian fields. However, due to its high dimensionality, large amount of data, data ambiguity, and susceptibility to the Hughes phenomenon, it is difficult to auto...

Claims

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Application Information

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IPC IPC(8): G06K9/66
CPCG06V30/194G06F18/2155
Inventor 王双亭王春阳郭增长张合兵成晓倩杨磊库
Owner HENAN POLYTECHNIC UNIV
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