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

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...

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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 information entropy, which solves the problem of insufficient utilization of a large number of unmarked labels in hyperspectral remote sensing images, and Improved classification accuracy and effectiveness

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