@article{MAKHILLJEAS202015118894,
    title = {A Novel Method for Remotely Sensed Hyperspectral Image Classification
Based on Convolutional Neural Network},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {15},
    number = {1},
    pages = {13-22},
    year = {2020},
    issn = {1816-949x},
    doi = {jeasci.2020.13.22},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2020.13.22},
    author = {B.R. and},
    keywords = {Convolution layer,convolutional neural networks,deep learning,fully connected network,hyperspectral image classification,land cover classification},
    abstract = {The Hyperspectral Images (HSI) acquired by remote sensors are characterized by hundreds of
contiguous channels with high spectral resolution. Hyperspectral image classification is the process of assigning
land cover classes to pixels. Classifying remotely sensed data is a challenge because many factors such as
complexity of landscape, image processing and classification approaches affect the success of classification.
Convolutional Neural Networks (CNN) are gaining attention due to their capability to automatically discover
relevant relative features in image classification problems. The proposed approach employs totally eight layers
in which four convolutional layers, two pooling layers and two Fully Connected Network (FCN) hidden layers
to extract features from hyperspectral images. This method is able to extract the features invariably for their
location and distortion which leads to better classification accuracy. By extracting both spatial and spectral
information, the performance of the model is a promising one for high dimensional HSI with few available
training data. This method has been applied to University of Pavia and Indian pines datasets. The resultant of
the model demonstrates very good classification accuracy within limited number of training epochs.}
    }