Blind nonlinear unmixing using nonnegative matrix factorization based bi-objective autoencoder

Sreejam Muraleedhara Bhakthan, Agilandeeswari Loganathan, Aashish Bansal


Hyperspectral image processing is one of the trending techniques used in many fields such as remote sensing, medical, agriculture, food processing, and military. The unique discrimination of hyperspectral images can be used for object identification, classification, and prediction. One of the main challenges of these tasks is the mixed pixel problem. Hyperspectral unmixing is the process of identifying the endmembers and their abundance in pixels. In linear unmixing, the mixture of the endmembers is assumed to be linear homogenous patches. Even though these models are simple and faster in performance, most of the real-world images are not linear. A modified nonlinear mixture-based sparsity regularized bi-objective autoencoder model based on nonnegative matrix factorization (NMF-BOA) is proposed in this article. The performance analysis shows that our model gives competitive results compared to the state-of-the-art models.


Bi-objective autoencoder; Endmember extraction; Hyperspectral image processing; Hyperspectral unmixing; Nonnegative matrix factorization

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The Indonesian Journal of Electrical Engineering and Computer Science (IJEECS)
p-ISSN: 2502-4752, e-ISSN: 2502-4760
This journal is published by the Institute of Advanced Engineering and Science (IAES) in collaboration with Intelektual Pustaka Media Utama (IPMU).

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