@article{MAKHILLAJIT20191866764,
    title = {Active Learning in Classification of Hyperspectral Imaging: A Review},
    journal = {Asian Journal of Information Technology},
    volume = {18},
    number = {6},
    pages = {173-179},
    year = {2019},
    issn = {1682-3915},
    doi = {ajit.2019.173.179},
    url = {https://makhillpublications.co/view-article.php?issn=1682-3915&doi=ajit.2019.173.179},
    author = {R.,K. and},
    keywords = {Hyperspectral image,active learning,classification,development,implemented,techniques},
    abstract = {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.}
    }