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Multi-Classification of Satellite Images


Shivan Nawal , Maharaja Agrasen Institute of Technology


Space Invariant, Artificial, Convolution, Neural Network


Every minute, the world loses an area of forest the size of 48 football fields. And deforestation in the Amazon Basin accounts for the largest share, contributing to reduced biodiversity, habitat loss, climate change, and other devastating effects. But better data about the location of deforestation and human encroachment on forests can help governments and local stakeholders respond more quickly and effectively. Convolution layers are used to extract the features from input training samples. Each convolution layer has a set of filters that helps in feature extraction. In general, as the depth of CNN model increases, complexity of features learnt by convolution layers increases. For example, first convolution layer captures simple features while the last convolution layer captures complex features of training samples. Another name for feed forward networks like these are Shift Invariant or Space Invariant Artificial Neural Networks (SIANN), as their architecture is mainly based on their weight sharing attribute and transient architecture. With upcoming Deep Learning techniques every year, the field of computer vision research has been advancing at a fast pace resulting in building robust models that have been created benchmarks on a world scale.

Other Details

Paper ID: IJSRDV6I20065
Published in: Volume : 6, Issue : 2
Publication Date: 01/05/2018
Page(s): 145-147

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