Fuzzy multiclass active learning for hyperspectral image classification. Issue 7 (1st July 2018)

• Record Type: Journal Article
• Authors: Samat, Alim; Gamba, Paolo; Liu, Sicong; Li, Erzhu; Miao, Zelang; Abuduwaili, Jilili
• Is Part Of: IET image processing. Volume 12:Issue 7(2018)
• Page Start: 1095
• Page End: 1101
• Publication Date: 2018-07-01
• Subjects: fuzzy set theory -- learning (artificial intelligence) -- hyperspectral imaging -- image classification -- possibility theory -- remote sensing -- geophysical image processing; fuzzy multiclass active learning -- hyperspectral image classification -- possibility theory -- fuzzy sets -- fuzzy logic -- active learning problems -- fuzzy multiclass AL algorithms -- fuzzy C‐order ambiguity; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2017.0784
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : The possibility theory, which is an extension of fuzzy sets and fuzzy logic, has shown considerable potential for solving active learning (AL) problems, particularly for multiclass scenarios' classification. Hence, two recently proposed fuzzy multiclass AL algorithms (classification ambiguity (CA) and fuzzy C‐order ambiguity (FCOA)) are investigated to properly generalise them for classifying hyperspectral images, and two improved versions of the CA and FCOA are proposed. In addition to comparing the performances of the original and improved algorithms, several other state‐of‐the‐art AL methods are evaluated, such as breaking ties, margin sampling, and multi‐class level uncertainty, with or without diversity criteria such as angle‐based diversity (ABD), clustering‐based diversity (CBD), and enhanced clustering‐based diversity (ECBD). Tests on two benchmark hyperspectral images confirm that the proposed improved algorithms are superior to and more effective than the original ones.