Open AccessExtraction of High-level and Low-level feature for classification of Image using Ridgelet and CNN based Image Classification
Author(s) -
T. Gladima Nisia,
S. Rajesh
Publication year - 2021
Publication title -
journal of physics: conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
ISSN - 1742-6588
DOI - 10.1088/1742-6596/1911/1/012019
Subject(s) - artificial intelligence , feature extraction , pattern recognition (psychology) , convolutional neural network , computer science , feature (linguistics) , image (mathematics) , contextual image classification , filter (signal processing) , computer vision , philosophy , linguistics
Remote Sensing image classification is an important research area for the recent time, because of its various application areas. Among the many available feature extraction methods, this paper uses the ridgelet based feature extraction method and those obtained features are combined with deep features obtained from the Convolutional Neural Network (CNN). Here, the Ridgelet’s are used to obtain the low-level features and CNN is used to obtain high-level feature. The system tries to construct the ridgelet filter for obtaining the low-level feature. The multi-resolution CNN is introduced using the concept of fusing high-level and low-level features via ridgelet and CNNs. Then, fused features are then classified and the output classified image is obtained. Experimental verifications are conducted on NWPU-RESISC45 dataset and the output results are provided to prove the best classification accuracies compared with the other proposed systems.