SPECTRAL AND SPATIAL UNMIXING OF HYPERSPECTRAL IMAGES USING SEMI-NONNEGATIVE MATRIX FACTORIZATION AND PRINCIPAL COMPONENT ANALYSIS

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ALIZADEH HABIB; Ghassemian hassan

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Journal: SIGNAL AND DATA PROCESSING Year:2015 | Volume:- | Issue:2 (SERIAL 22) Page(s): 57-70

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Title

SPECTRAL AND SPATIAL UNMIXING OF HYPERSPECTRAL IMAGES USING SEMI-NONNEGATIVE MATRIX FACTORIZATION AND PRINCIPAL COMPONENT ANALYSIS

Author(s)

ALIZADEH HABIB | Ghassemian hassan | Issue Writer Certificate

Keywords

HYPERSPECTRAL IMAGESQ2

REMOTE SENSING DATA UNMIXINGQ2

BLIND SOURCE SEPARATIONQ3

SEMI-NONNEGATIVE MATRIX FACTORIZATIONQ2

PRINCIPAL COMPONENT ANALYSISQ1

Abstract

Unmixing of remote-sensing data using nonnegative matrix factorization has been considered recently. To improve performance, additional constraints are added to the cost function. The main challenge is to introduce constraints that lead to better results for unmixing.Correlation between bands of HYPERSPECTRAL IMAGES is the problem that is paid less attention to it in the unmixing algorithms. In this paper, we have proposed a new method for unmixing of Hyperspectral data using SEMI-NONNEGATIVE MATRIX FACTORIZATION and PRINCIPAL COMPONENT ANALYSIS. In the proposed method, spectral and spatial unmixing is performed simultaneously. Physical constraints applied based on Linear Mixing Model. In addition to physical constraints, characteristics of Hyperspectral data have been exploited in the unmixing process. Sparseness of the abundance is one of the important features of Hyperspectral data, which is applied using the nsNMF matrix. In the proposed method update rules is derived using the ALS algorithm. In the final section of this paper, real and synthetic Hyperspectral data is used to verify the effectiveness of the proposed algorithm. Obtained results show the superiority of the proposed algorithm in comparison with some unmixing algorithms.

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APA: Copy

ALIZADEH, HABIB, & Ghassemian, hassan. (2015). SPECTRAL AND SPATIAL UNMIXING OF HYPERSPECTRAL IMAGES USING SEMI-NONNEGATIVE MATRIX FACTORIZATION AND PRINCIPAL COMPONENT ANALYSIS. SIGNAL AND DATA PROCESSING, -(2 (SERIAL 22)), 57-70. SID. https://sid.ir/paper/160839/en

Vancouver: Copy

ALIZADEH HABIB, Ghassemian hassan. SPECTRAL AND SPATIAL UNMIXING OF HYPERSPECTRAL IMAGES USING SEMI-NONNEGATIVE MATRIX FACTORIZATION AND PRINCIPAL COMPONENT ANALYSIS. SIGNAL AND DATA PROCESSING[Internet]. 2015;-(2 (SERIAL 22)):57-70. Available from: https://sid.ir/paper/160839/en

IEEE: Copy

HABIB ALIZADEH, and hassan Ghassemian, “SPECTRAL AND SPATIAL UNMIXING OF HYPERSPECTRAL IMAGES USING SEMI-NONNEGATIVE MATRIX FACTORIZATION AND PRINCIPAL COMPONENT ANALYSIS,” SIGNAL AND DATA PROCESSING, vol. -, no. 2 (SERIAL 22), pp. 57–70, 2015, [Online]. Available: https://sid.ir/paper/160839/en

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