Authors :
Kavita Bhosle, Vijaya Musande
Volume/Issue :
Volume 5 - 2020, Issue 1 - January
Google Scholar :
https://goo.gl/DF9R4u
Scribd :
https://bit.ly/30H0eRu
Abstract :
In this paper we have compared regularized
Convolutional Neural Network (CNN) with without
regularized CNN. This model is now becomes popular
for many applications. It is used for classification,
identification of object. The main advantage Deep
learning CNN is that it can be used for unstructured
video, audio, image data. Remote sensing data is highly
unstructured data. EO-1 hyperspectral data has been
used for the study of crop classification. It has been
observed that classification accuracy is 75 % and test
loss is 43.3 %.
Keywords :
Convolutional Neural Network, Deep Learning, Land Use Land Cover.
In this paper we have compared regularized
Convolutional Neural Network (CNN) with without
regularized CNN. This model is now becomes popular
for many applications. It is used for classification,
identification of object. The main advantage Deep
learning CNN is that it can be used for unstructured
video, audio, image data. Remote sensing data is highly
unstructured data. EO-1 hyperspectral data has been
used for the study of crop classification. It has been
observed that classification accuracy is 75 % and test
loss is 43.3 %.
Keywords :
Convolutional Neural Network, Deep Learning, Land Use Land Cover.
