TY  - JOUR
T1  - Active Learning in Classification of Hyperspectral Imaging: A Review
AU - Elakkiya, R. AU - Thilagavathi, K. AU - Vasuki, A. 
JO  - Asian Journal of Information Technology
VL  - 18
IS  - 6
SP  - 173
EP  - 179
PY  - 2019
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2019.173.179
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2019.173.179
KW  - Hyperspectral image
KW  -active learning
KW  -classification
KW  -development
KW  -implemented
KW  -techniques
AB  - Hyperspectral images are used to characterize
the objects with unprecedented accuracy of the data. The
active learning aims at providing efficient training set by
iterating the samples. This study reviews the concepts
involved in active learning algorithm for classification of
remote sensing image or hyperspectral image. The
diversified vision of hyperspectral sensors was awakened
with the latest development of remote sensing and
geographical information. Imaging spectroscopy which is
commonly known as hyperspectral remote sensing was
recently inspected by researchers and scientists for
exploring vegetations, minerals, etc. This hyperspectral
imaging requires large data sets and new processing
techniques. Several active learning algorithms are
implemented in hyperspectral images for better
classification and greater accuracy.
ER  -