Paper Info
Title
A Deep Rule-Based Approach For Satellite Scene Image Analysis
Abstract
Satellite scene images contain multiple sub-regions of different land use categories; however, traditional approaches usually classify them into a particular category only. In this paper, a new approach is proposed for automatically analyzing the semantic content of sub-regions of satellite images. At the core of the proposed approach is the recently introduced deep rule-based image classification method. The proposed approach includes a self-organizing set of transparent zero order fuzzy IF THEN rules with human-interpretable prototypes identified from the training images and a pre-trained deep convolutional neural network as the feature descriptor. It requires a very short, nonparametric, highly parallelizable training process and can perform a highly accurate analysis on the semantic features of local areas of the image with the generated IF-THEN rules in a fully automatic way. Examples based on benchmark datasets demonstrate the validity and effectiveness of the proposed approach.
Year
DOI
Venue
2018
10.1109/SMC.2018.00474
2018 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN, AND CYBERNETICS (SMC)
Keywords
Field
DocType
deep learning, deep fuzzy rule-based classifier, image analysis
Parallelizable manifold,Rule-based system,Satellite,Pattern recognition,Convolutional neural network,Computer science,Fuzzy logic,Nonparametric statistics,Artificial intelligence,Deep learning,Contextual image classification,Machine learning
Conference
ISSN
Citations 
PageRank 
1062-922X
0
0.34
References 
Authors
0
2
Name
Order
Citations
PageRank
Xiaowei Gu19910.96
Plamen Angelov295467.44