NEURAL NETWORK HYPERSPECTRAL UNMIXING WITH SPECTRAL INFORMATION DIVERGENCE OBJECTIVE

被引：0

作者：

Palsson, Frosti ^{[1
]}

Sigurdsson, Jakob ^{[1
]}

Sveinsson, Johannes R. ^{[1
]}

Ulfarsson, Magnus O. ^{[1
]}

机构：

[1] Univ Iceland, Fac Elect & Comp Engn, Hjardarhagi 2-6, IS-107 Reykjavik, Iceland

来源：

2017 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS) | 2017年

关键词：

Hyperspectral unmixing; autoencoder; deep learning; neural network; spectral information divergence; SPARSE REGRESSION;

D O I：

暂无

中图分类号：

P [天文学、地球科学];

学科分类号：

07 ;

摘要：

Hyperspectral unmixing is a challenging inverse problem that involves determining the fractional abundances of the representive material (endmembers) in each pixel. In this paper, we develop a neural network autoencoder, that dynamically exploits the sparsity of the abundances and enforces the abundance sum constraint (ASC) for hyperspectral unmixing. Instead of using the conventional mean square error (MSE) objective function, we use the spectral information divergence (SID) measure. Experiments are performed using a real hyperspectral dataset and we compare results obtained using both MSE and SID. It is demonstrated by qualitative inspection that using SID gives significantly better results than using MSE.

引用

页码：755 / 758

页数：4

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