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HNMF remote sensing image unmixing algorithm based on information entropy

A remote sensing image and information entropy technology, applied in the field of HNMF remote sensing image unmixing algorithm, can solve the problem of not considering the physical information of remote sensing data, and achieve good unmixing effect

Active Publication Date: 2019-11-15
CHINA UNIV OF GEOSCIENCES (WUHAN)
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] However, the above optimization algorithms only consider the spectral information and spatial information of remote sensing data, and most of them introduce L 1 , L 2 The constraints of equal norms are used to optimize the algorithm, and the physical information of remote sensing data is not considered

Method used

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  • HNMF remote sensing image unmixing algorithm based on information entropy
  • HNMF remote sensing image unmixing algorithm based on information entropy
  • HNMF remote sensing image unmixing algorithm based on information entropy

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Experimental program
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Embodiment 1

[0070] Embodiment 1. The initial value is obtained by a random method, and the parameters of the CNMF algorithm are empirical values, and the regularization parameters of the HNMF algorithm also refer to the empirical values ​​of CNMF. Three kinds of ground features, Carnallite, Chert and Andradite, were selected as the experimental results, which are the average results of 25 experiments.

[0071] Such as figure 2 As shown, the comparison between the three endmember abundance maps unmixed by the three algorithms of NMF, CNMF and HNMF and the real endmember abundance map. From the part circled by the white rectangle in the figure, the endmember abundance map unmixed by the HNMF algorithm is closest to the real endmember abundance map.

[0072] As shown in Table 1, it shows the comparison of the SAD value and RMSE value of the unmixing results of these three algorithms. From the results, the HNMF algorithm is better than the two performance indicators of spectral angular dist...

Embodiment 2

[0075] Embodiment 2. The data used is part of the data of Cuprite in Nevada, USA. Through real data experiments, the unmixing of the three algorithms of NMF, CNMF and HNMF is further compared and analyzed. The abundance map is based on the results of the tetracorder operation. , for the spectral curve of the end member, the spectral curve of the end member was extracted from the original data on the ENVI4.8 platform as a reference result, from which hematite, chalcedony and pyroxene were selected. A typical ground feature is used as a reference result, such as image 3 As shown in Table 2, the abundance maps, SAD values ​​and RMSE values ​​of the three endmembers after the unmixing of the three algorithms are given in turn.

[0076] From the abundance projection map, for hematite, the area circled by the white frame in the second column in the table, from top to bottom, the color is getting closer to black, which is closer to the reference result; for chalcedony, the third col...

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Abstract

The invention provides an HNMF remote sensing image unmixing algorithm based on information entropy. The algorithm comprises the steps of S1, establishing a sparse constraint-based CNMF remote sensingimage unmixing algorithm for a current remote sensing image; s2, obtaining the information entropy of the current remote sensing image, and obtaining an information entropy regularization function; and S3, replacing a norm regularization function in the sparse constraint-based CNMF remote sensing image unmixing algorithm in the step S1 with the information entropy regularization function in the step S2, and establishing an information entropy-based HNMF remote sensing image unmixing algorithm. Aiming at the characteristic of uneven distribution of end members of a remote sensing image, physical information of remote sensing data is mined, norm rule items are replaced by information entropy, an HNMF remote sensing image unmixing algorithm based on the information entropy is provided, and compared with traditional NMF and CNMF, a better unmixing effect is achieved.

Description

technical field [0001] The invention relates to an HNMF remote sensing image unmixing algorithm based on information entropy. Background technique [0002] Remote sensing technology is a new technology that intersects multiple disciplines such as detection and information detection. As the research and development of imaging spectrometers continues to make new breakthroughs, image analysis technology is getting more and more in-depth, and the research and development of remote sensing technology has set off an upsurge, which has won the favor of the majority of scientific researchers and occupies a place in various fields. [0003] The prototype of remote sensing technology is the imaging spectrometer research and development program, which was first formulated and proposed by the Jet Propulsion Lab (JPL) of the California Institute of Technology. The world's first generation of high-resolution aerial imaging spectrometer - AIS-1 was born in the United States in 1983. The ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T5/00G06T7/00
CPCG06T7/0002G06T2207/10032G06T5/70
Inventor 李杏梅刘晓杰王心宇
Owner CHINA UNIV OF GEOSCIENCES (WUHAN)