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Hyper-spectral remote sensing image semi-supervised classification method based on ground object class membership grading

A technology of hyperspectral remote sensing and classification methods, which is applied in the field of semi-supervised classification of hyperspectral remote sensing images, can solve problems such as the degree of diversity aggravated by multiple scattering effects, the phenomenon of different objects with the same spectrum, and classification difficulties, and achieve high-quality classification results. High classification accuracy, achieve the effect of fuzzy scoring

Inactive Publication Date: 2015-03-25
FUDAN UNIV
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Problems solved by technology

In addition, factors such as low spatial resolution, heterogeneity of surface object distribution, and multiple scattering effects will aggravate the degree of diversity [2], which often leads to the phenomenon of different spectra of the same object or the phenomenon of different spectra of different objects, which makes classification difficult

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  • Hyper-spectral remote sensing image semi-supervised classification method based on ground object class membership grading
  • Hyper-spectral remote sensing image semi-supervised classification method based on ground object class membership grading
  • Hyper-spectral remote sensing image semi-supervised classification method based on ground object class membership grading

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Embodiment Construction

[0061] Below, take actual remote sensing image data as an example to illustrate the specific embodiment of the present invention:

[0062] The semi-supervised classification method based on membership score in the present invention is denoted by SCAS, and the two modes that adopt SLIC and cube over-segmentation are denoted by SCAS1 and SCAS2 respectively.

[0063] real data experiment

[0064] We test the performance of the proposed algorithm using an actual hyperspectral remote sensing image dataset. The data set is the Indian Pines data set taken in 1992 by the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS). The dataset contains 145×145 pixels, 220 bands, the wavelength range is 0.4-2.5μm, and the spectral resolution is 10nm. After removing bands with low SNR or water absorption, the remaining 186 bands were used for algorithm validation. figure 1 A pseudo-color map of the image is shown, along with a map of the true distribution of features. According to the fi...

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Abstract

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a hyper-spectral remote sensing image semi-supervised classification method based on ground object class membership grading. On the premise of over-segmentation, membership grading serves as a kernel, region growing procedures are introduced, spectral information and space information are effectively combined, and a new strategy is provided for semi-supervised classification, wherein the fuzzy theory serves as the basis of membership grading, and three factors, namely spatial consistency of hyper-spectral images, spectrum variability and prior knowledge, are synchronously weighed so that a high-precision classification result and a smooth classification identification graph can be obtained. The method has good robustness in terms of the occupied ratio of parameters and training samples in total samples. The prior knowledge is efficiently used in fuzzy grading of ground object class membership, only a few training samples are needed to output the high-quality classification result, and classification precision is not sensitive to changes of the parameters. The method has important application value in classification of the hyper-spectral images.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a semi-supervised classification method for hyperspectral remote sensing images based on scoring of the membership degree of ground object categories. Background technique [0002] Remote sensing technology is a new comprehensive technology developed in the 1960s. It is closely related to science and technology such as space, electron optics, computer, and geography. It is one of the most powerful technical means for studying the earth's resources and environment. Hyperspectral remote sensing is a multi-dimensional information acquisition technology that combines imaging technology with spectral technology. The hyperspectral imager simultaneously detects the two-dimensional geometric space and one-dimensional spectral information of the target on dozens to hundreds of very narrow and continuous spectral segments of the electromagnetic spectrum,...

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Application Information

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IPC IPC(8): G06K9/62
CPCG06F18/24133
Inventor 陈昭王斌
Owner FUDAN UNIV
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