RT Journal Article
A1 Alim Samat
A1 Paolo Gamba
A1 Sicong Liu
A1 Erzhu Li
A1 Zelang Miao
A1 Jilili Abuduwaili

PB iet
T1 Fuzzy multiclass active learning for hyperspectral image classification
JN IET Image Processing
VO 12
IS 7
SP 1095
OP 1101
AB 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.
K1 possibility theory
K1 fuzzy C-order ambiguity
K1 fuzzy multiclass AL algorithms
K1 fuzzy sets
K1 active learning problems
K1 fuzzy logic
K1 hyperspectral image classification
K1 fuzzy multiclass active learning
DO https://doi.org/10.1049/iet-ipr.2017.0784
UL https://digital-library.theiet.org/;jsessionid=db8bj70k1b2op.x-iet-live-01content/journals/10.1049/iet-ipr.2017.0784
LA English
SN 1751-9659
YR 2018
OL EN