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Hyperspectral remote sensing image classifying method based on hierarchy ensemble learning

A technology of hyperspectral remote sensing and classification method, which is applied in the fields of instruments, character and pattern recognition, computer parts, etc. The effect of classification accuracy

Inactive Publication Date: 2015-01-07
HARBIN INST OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0004] The purpose of the present invention is to solve the problem of low classification accuracy of hyperspectral remote sensing image data, and provide a hyperspectral remote sensing image classification method based on layered ensemble learning

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  • Hyperspectral remote sensing image classifying method based on hierarchy ensemble learning

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specific Embodiment approach 1

[0032] Specific implementation mode one: the following combination figure 1 Describe this embodiment, the hyperspectral remote sensing image classification method based on hierarchical integrated learning described in this embodiment, it comprises the following steps:

[0033] Step 1: Read the hyperspectral raw data, preprocess it, obtain supervised data, then determine the labeled samples according to the supervised data, and select training samples and test samples from the labeled samples;

[0034] Step 2: Perform random band selection for all spectral bands of the hyperspectral raw data, and then extract a pixel vector combination from each marked sample, the number of bands contained in each pixel vector in the pixel vector combination is the same as that of the random The number of bands corresponds one by one to form a spectral set; repeat the random band selection process and form a corresponding spectral set to form multiple spectral sets with differences;

[0035] S...

specific Embodiment approach 2

[0039] Specific implementation mode two: this implementation mode further explains implementation mode one, the specific method of determining the marked samples in step one, and selecting training samples and test samples from the marked samples is as follows:

[0040] Read the hyperspectral raw data stored in the form of a three-dimensional matrix, the three-dimensional matrix form includes two-dimensional spatial position information and one-dimensional spectral information; The object categories in the marker map are respectively marked in the form of integer values, and the supervision data in the form of a two-dimensional matrix is ​​obtained. composition;

[0041] From the hyperspectral raw data and supervisory data, determine the number of feature categories C in the real feature marker map, the number of pixels in the two-dimensional spatial position information M rows × N columns, and the number of effective bands in the one-dimensional spectral information B;

[004...

specific Embodiment approach 3

[0047] Specific implementation mode three: the following combination figure 1 Describe this embodiment, this embodiment will further explain Embodiment 2, and the specific method for forming multiple spectral sets with differences in Step 2 is as follows:

[0048] Draw a set of b random numbers between 1 and B {i 1 ,i 2 ,...,i b}, where 1≤i k ≤B, k=1,2,...,b), and then from each labeled sample, the i-th k The vector elements are extracted to form a pixel vector combination containing b elements respectively, the number of the pixel vector combination is the same as the number of marked samples, and all the pixel vector combinations containing b elements are used as a spectral set;

[0049] The process of forming a spectrum set is repeated multiple times, and b random numbers are selected in the repeated process, and each combination of random numbers is different, so as to obtain multiple spectrum sets with differences.

[0050] The hyperspectral raw data contains dozens ...

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Abstract

The invention belongs to the technical field of hyperspectral remote sensing image classifying, and relates to a hyperspectral remote sensing image classifying method based on hierarchy ensemble learning to solve the problem that hyperspectral remote sensing image data are low in classification precision. The method mainly comprises the steps that two layers of integrated structures are used for classifying hyperspectral images and include an inner structure and an outer structure; in the inner structure, spectrum sets with differences are formed through random wave band selection; the spectrum sets serve as a unit, training is carried out through an Adaboost integrating method, and testing samples are classified; in the outer structure, the classification results of the spectrum sets in the inner set are integrated, and the final classification of the samples is determined through a weighted voting method; the whole image serves as a testing sample, the whole image is classified, and then a classified topic image is obtained. The method is used for classifying the hyperspectral remote sensing image.

Description

technical field [0001] The invention relates to a hyperspectral remote sensing image classification method based on hierarchical integrated learning, and belongs to the technical field of hyperspectral remote sensing image classification. Background technique [0002] With the improvement of remote sensing data acquisition methods, hyperspectral remote sensing images have become the key research content due to their unique advantages. Hyperspectral remote sensing can acquire many very narrow and spectrally continuous image data in the ultraviolet, visible, near-infrared and mid-infrared regions of the electromagnetic spectrum, and has the characteristics of combining images and spectra. This has important research value and application significance for using remote sensing images for target classification, target recognition, and target tracking. At present, the classification of target objects using hyperspectral remote sensing data is one of the hot applications of hypers...

Claims

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24G06F18/214
Inventor 陈雨时赵兴刘柏森
Owner HARBIN INST OF TECH
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