Non-Negative Minimum Volume Factorization (NMVF) for Hyperspectral Images (HSI) Unmixing: A Hybrid Approach

作     者：Kriti Mahajan Urvashi Garg Nitin Mittal Yunyoung Nam Byeong-Gwon Kang Mohamed Abouhawwash 

作者机构：Department of Computer Science and EngineeringChandigarh UniversityPunjab140413India Skill Faculty of Science and TechnologyShri Vishwakarma Skill UniversityPalwal121102India Department of Computer Science and EngineeringSoonchunhyang UniversityAsan31538Korea Department of ICT ConvergenceSoonchunhyang UniversityAsan31538Korea Department of MathematicsFaculty of ScienceMansoura UniversityMansoura35516Egypt Department of Computational MathematicsScienceand Engineering(CMSE)College of EngineeringMichigan State UniversityEast LansingMI48824USA 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第73卷第11期

页      面：3705-3720页

核心收录：

学科分类：07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724 The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund 

主　　题：Hyperspectral Imaging minimum volume simplex source separation end member extraction non-negative minimum volume factorization(NMVF) endmembers(EMs) 

摘      要：Spectral unmixing is essential for exploitation of remotely senseddata of Hyperspectral Images (HSI). It amounts to the identification of a position of spectral signatures that are pure and therefore called end members andtheir matching fractional, draft rules abundances for every pixel in HSI. Thispaper aims to unmix hyperspectral data using the minimal volume methodof elementary scrutiny. Moreover, the problem of optimization is solved bythe implementation of the sequence of small problems that are constrainedquadratically. The hard constraint in the final step for the abundance fractionis then replaced with a loss function of hinge type that accounts for outlinersand noise. Existing algorithms focus on estimating the endmembers (Ems)enumeration in a sight, discerning of spectral signs of EMs, besides assessmentof fractional profusion for every EM in every pixel of a sight. Nevertheless, allthe stages are performed by only a few algorithms in the process of hyperspectral unmixing. Therefore, the Non-negative Minimum Volume Factorization(NMVF) algorithm is further extended by fusing it with the nonnegativematrix of robust collaborative factorization that aims to perform all the threeunmixing chain steps for hyperspectral images. The major contributions ofthis article are in this manner: (A) it performs Simplex analysis of minimum volume for hyperspectral images with unsupervised linear unmixing isemployed. (B) The simplex analysis method is configured with an exaggeratedform of the elementary which is delivered by vertical component analysis(VCA). (C) The inflating factor is chosen carefully inactivating the constraintsin a large majority for relating to the source fractions abundance that speedsup the algorithm. (D) The final step is making simplex analysis method robustto outliners as well as noise that replaces the profusion element positivity hardrestraint by a hinge kind soft restraint, preserving the local minima havinggood quality. (E) The matrix factorizati