Open AccessClassification of remote sensing images using CNN
Author(s) -
Aswathy K. Cherian,
E. Poovammal
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1130/1/012084
Subject(s) - computer science , convolutional neural network , remote sensing , focus (optics) , contextual image classification , artificial intelligence , range (aeronautics) , remote sensing application , deep learning , data classification , pattern recognition (psychology) , data mining , image (mathematics) , geography , engineering , hyperspectral imaging , physics , aerospace engineering , optics
A number of factors can influence the classification of images. The effective outcome or valuable information from multi-source data for better earth exploration becomes a fascinating yet challenging problem when a list of remotely sensed data sources is available. Efficient Remote Sensing Images (RSI) classification has been the foundation for remote sensing applications. A range of classifiers are used to realize the classification of several types of remote sensing image targets. Better classification of targets is critical in both military and civilian sectors. In this paper, the focus is on summarizing the main advanced approaches used to enhance classification precision. The result shows that the Convolutional Neural Network outperforms all the other traditional classification methods.