Search Results for author: Hassan Ghassemian

Found 6 papers, 1 papers with code

Deep Curriculum Learning for PolSAR Image Classification

no code implementations26 Dec 2021 Hamidreza Mousavi, Maryam Imani, Hassan Ghassemian

Following the great success of curriculum learning in the area of machine learning, a novel deep curriculum learning method proposed in this paper, entitled DCL, particularly for the classification of fully polarimetric synthetic aperture radar (PolSAR) data.

Classification Image Classification

Multilayer Structured NMF for Spectral Unmixing of Hyperspectral Images

no code implementations4 Jun 2015 Roozbeh Rajabi, Hassan Ghassemian

Sparseness constraint on both spectral signatures and abundance fractions matrices are used in this paper.

Sparsity Constrained Graph Regularized NMF for Spectral Unmixing of Hyperspectral Data

no code implementations3 Nov 2014 Roozbeh Rajabi, Hassan Ghassemian

The presence percentages of endmembers in mixed pixels are called abundance fractions.

Spectral Unmixing of Hyperspectral Imagery using Multilayer NMF

1 code implementation12 Aug 2014 Roozbeh Rajabi, Hassan Ghassemian

In this letter we proposed using multilayer NMF (MLNMF) for the purpose of hyperspectral unmixing.

Hyperspectral Unmixing

Hyperspectral Data Unmixing Using GNMF Method and Sparseness Constraint

no code implementations29 Jun 2013 Roozbeh Rajabi, Hassan Ghassemian

In this paper we have used graph regularized (GNMF) method with sparseness constraint to unmix hyperspectral data.

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