Interpretable Rule-Based Fuzzy ELM and Domain Adaptation for Remote Sensing Image Classification

The size of the remote sensing (RS) data is increasing exponentially with modern technological evolution. Efficient mapping with these data using a supervised model demands ample labeled samples, and obtaining them is expensive and time-consuming. For that reason, domain adaptation (DA) methodologie...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2021-07, Vol.59 (7), p.5907-5919
Main Authors: Meher, Saroj K., Kothari, Neeta S.
Format: Article
Language:English
Subjects:
Adaptation
Adaptation models
Artificial neural networks
Classification
Computer architecture
Data
Data models
Datasets
domain adaptation
Domains
Extreme learning machine
Feature extraction
fuzzy inference system
Image classification
Indexes
Knowledge management
land cover classification
Learning algorithms
Machine learning
Performance indices
Performance measurement
Remote sensing
Task analysis
Training
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The size of the remote sensing (RS) data is increasing exponentially with modern technological evolution. Efficient mapping with these data using a supervised model demands ample labeled samples, and obtaining them is expensive and time-consuming. For that reason, domain adaptation (DA) methodologies that work with limited available knowledge in terms of labeled samples or trained models are highly successful. DA uses the model trained for a domain called "source" and aims to classify the data of a related domain called "target." However, an insufficient amount of labeled samples in the target domain data set is still a bottleneck for DA models used for RS images. To address this, we have proposed an efficient DA classification model using an interpretable rule-based fuzzy extreme learning machine (IRF-ELM). These rules are derived using the maximum fuzzy membership value of features characterized by class-belonging fuzzification and two rule extraction matrices. A partially connected ELM architecture is then designed with these rules. The fuzzification process typically aligns the target domain's feature space to the source domain in the best possible ways to transfer knowledge between them. The proposed model's superiority to similar other methods is verified using different RS data sets in terms of various performance measurement indexes.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2020.3024796