Remote sensing image mixed image element decomposition method based on self-organizing mapping neural network

A technology of mixed pixel decomposition and self-organizing mapping, which is applied in the field of remote sensing image processing, can solve the problems of large amount of calculation, easy to fall into local extreme points, etc., and achieve the effect of ensuring robustness and speed

Inactive Publication Date: 2008-07-16
FUDAN UNIV
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However, the iterative process of the fuzzy c-means clustering algorithm has the

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  • Remote sensing image mixed image element decomposition method based on self-organizing mapping neural network
  • Remote sensing image mixed image element decomposition method based on self-organizing mapping neural network
  • Remote sensing image mixed image element decomposition method based on self-organizing mapping neural network

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Embodiment approach

[0080] 1. Simulate remote sensing image data

[0081] 1) AVIRIS hyperspectral artificial remote sensing data

[0082] First, three mineral endmembers were selected from the AVIRIS hyperspectral mineral endmember spectral library. Figure 6 shows the spectral curves of these three mineral endmembers. Afterwards, three standard abundance value matrices (32×32) satisfying the non-negative constraint of abundance value and the constraint of abundance value sum to 1 are randomly generated, and the endmembers are mixed according to the randomly generated standard abundance value matrix to obtain Blend images as artificial remote sensing images. In the experiment, the artificial remote sensing image was decomposed into mixed pixels, and the decomposed abundance value matrix was compared with the randomly generated standard abundance value matrix to quantitatively evaluate the decomposition accuracy. Root Mean Square Error (Root Mean Square Error, RMSE) and Correlation Coefficient (C...

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Abstract

The invention belongs to the remote sensing image processing technical field, in particular to a remote sensing image mixed pixel decomposition method based on self-organization mapping neural network. The method integrates the self-organization mapping neural network and the fuzzy grade of membership in the fuzzy theory to work out abundance values after decomposition. Simultaneously, as the self-organization mapping neural network does not has the competitive learning characteristic of an objective function, the method is free from the problem of local extremum. In addition, the invention automatically meets the two bindings required by the mixed pixel decomposition problem, wherein the two binds are respectively the binding of non-negative abundance values and the binding of the sum of the abundance values being 1. The invention has a better mixed pixel decomposition effect and a higher anti-noise capacity. The new method has a particularly important application value concerning the high-accuracy ground object separation and ground target detection and identification based on multi-spectrum and high spectrum remote sensing image.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a remote sensing image mixed pixel decomposition method based on self-organizing mapping neural network and fuzzy membership degree. 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 ground surface, a pixel in a remote sensing image often corresponds to a large area on the ground, and there may be many types of ground objects in this area, which fo...

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Application Information

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IPC IPC(8): G06T7/00
Inventor 刘力帆王斌张立明
Owner FUDAN UNIV
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