TELKOMNIKA Telecommunication Computing Electronics and Control
Competent scene classification using feature fusion of pre-trained convolutional neural networks

TELKOMNIKA Telecommunication Computing Electronics and Control
Competent scene classification using feature fusion of pre-trained convolutional neural networks

Feature extraction
Feature fusion
Pre-trained networks
Scene classification
Support vector machine

In view of the fact that the development of convolutional neural networks
(CNN) and other deep learning techniques, scientists have become more
interested in the scene categorization of remotely acquired images as well as
other algorithms and datasets. The spatial geometric detail information may
be lost as the convolution layer thickness increases, which would have a
significant impact on the classification accuracy. Fusion-based techniques,
which are regarded to be a viable way to express scene features, have
recently attracted a lot of interest as a solution to this issue. Here,
we suggested a convolutional feature fusion network that makes use of
canonical correlation, which is the linear correlation between two feature
maps. Then, to improve scene classification accuracy, the deep features
extracted from various pre-trained convolutional neural networks are
efficiently fused. We thoroughly evaluated three different fused CNN
designs to achieve the best results. Finally, we used the support vector
machine for categorization (SVM). In the analysis, two real-world datasets
UC Merced and SIRI-WHU were employed, and the competitiveness of the
investigated technique was evaluated. The improved categorization accuracy
demonstrates that the fusion technique under consideration has produced
affirmative results when compared to individual networks.

Thirumaladevi Satharajupalli, Kilari Veera Swamy, Maruvada Sailaja

http://telkomnika.uad.ac.id

Feb 16, 2023

peri irawan

pdf

english

text