Spatially resolved spectral unmixing using convolutional neural networks

被引：3

作者：

Anastasiadis, Johannes ^{[1
]}

Leon, Fernando Puente ^{[1
]}

机构：

[1] Karlsruher Inst Technol, Inst Ind Informat Tech, Karlsruhe, Germany

来源：

TM-TECHNISCHES MESSEN | 2019年 / 86卷

关键词：

Hyperspectral image; spectral unmixing; convolutional neural networks; MATERIAL ABUNDANCES; FOOD;

D O I：

10.1515/teme-2019-0062

中图分类号：

TH7 [仪器、仪表];

学科分类号：

0804 ; 080401 ; 081102 ;

摘要：

This paper proposes a model-free approach for spectral unmixing based on a convolutional neural network. It is evaluated using sample data sets and compared with methods based on the linear mixing model. Furthermore, methods to ensure physical plausibility of the estimated abundances are presented. This approach involves output layers that enforce non-negativity and the sum-to-one constraint.

引用

页码：S122 / S126

页数：5

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