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Hyperspectral remote sensing image mixed pixel decomposition method based on independent component analysis

An independent component analysis, hyperspectral remote sensing technology, applied in the field of remote sensing image processing

Inactive Publication Date: 2010-10-20
FUDAN UNIV
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Problems solved by technology

The role of AAM is to overcome the shortcomings of traditional ICA that can only describe fixed distributions, so that the results can be closer to the ideal value; the constraints are used to adjust the objective function of the traditional ICA algorithm, so that the algorithm is physically suitable for hyperspectral mixed pixel solutions mixed problem

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  • Hyperspectral remote sensing image mixed pixel decomposition method based on independent component analysis
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  • Hyperspectral remote sensing image mixed pixel decomposition method based on independent component analysis

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Embodiment approach

[0125] 1 simulation data

[0126] We compare and analyze the CICA method proposed by the present invention with the following three typical algorithms: HOS-ICPA[11], VCA[12], and MVCNMF[13]. Among them, the VCA algorithm can only obtain the spectral matrix, so after solving the spectrum, use FCLS[14] to obtain the abundance matrix, and this method is recorded as VCA-FCLS. Use simulation data to test the performance of all the above algorithms, and use SAD and RMSE to measure the difference between the results solved by all algorithms and the real reference value.

[0127] Spectral Angle Distance (Spectral Angle Distance, SAD) is used to measure the degree of difference between the spectrum solved by the algorithm and the known reference spectrum, the real spectral vector a of the i-th end member i =[a i1 ,...,a ik ,...,a iL ] T The corresponding unmixing result The SAD between is defined as

[0128] SAD i = arccos ...

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Abstract

The invention belongs to the technical field of remote sensing image processing, in particular to a hyperspectral remote sensing image mixed pixel decomposition method based on independent component analysis. According to the physical characteristics of hyperspectral images, the invention introduces abundance sum-to-one constraint and abundance nonnegative constraint into the target function of independent component analysis, and provides a self-adapting abundance modeling method to describe probability distribution of data, thus presenting favorable applicability towards different remote data. The method can effectively solve the problems of high mixing degree and remote sensing data mixed pixel decomposition under various interferences, and has significant application value in high-precision ground feature classification and detection and recognition of ground targets based on multispectral and hyperspectral remote images.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a method based on independent component analysis, which can solve the problem of decomposing mixed pixels of highly mixed remote sensing data. Background technique [0002] Remote sensing 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. In recent years, with the advancement of imaging technology, multi-band remote sensing images have been widely used in more and more fields. Due to the limitation of the spatial resolution of the imaging system and the complexity and variety of the surface, a pixel in the obtained remote sensing image often contains multiple types of ground objects, which forms a mixed pixel. How to extract the spectru...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06T7/00
Inventor 夏威王斌张立明
Owner FUDAN UNIV
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