Not logged in : Login
(Sponging disallowed)

About: A Semi-supervised Deep Rule-Based Approach for Remote Sensing Scene Classification     Goto   Sponge   Distinct   Permalink

An Entity of Type : bibo:AcademicArticle, within Data Space : demo.openlinksw.com associated with source document(s)

AttributesValues
type
seeAlso
sameAs
Title
  • A Semi-supervised Deep Rule-Based Approach for Remote Sensing Scene Classification
  • A Semi-supervised Deep Rule-Based Approach for Remote Sensing Scene Classification
described by
Date
  • 2019-04-18
  • 2019-04-18
Creator
status
Publisher
abstract
  • This paper proposes a new approach that is based on the recently introduced semi-supervised deep rule-based classifier for remote sensing scene classification. The proposed approach employs a pre-trained deep convoluational neural network as the feature descriptor to extract high-level discriminative semantic features from the sub-regions of the remote sensing images. This approach is able to self-organize a set of prototype-based IF...THEN rules from few labeled training images through an efficient supervised initialization process, and continuously self-updates the rule base with the unlabeled images in an unsupervised, autonomous, transparent and human-interpretable manner. Highly accurate classification on the unlabeled images is performed at the end of the learning process. Numerical examples demonstrate that the proposed approach is a strong alternative to the state-of-the-art ones.
  • This paper proposes a new approach that is based on the recently introduced semi-supervised deep rule-based classifier for remote sensing scene classification. The proposed approach employs a pre-trained deep convoluational neural network as the feature descriptor to extract high-level discriminative semantic features from the sub-regions of the remote sensing images. This approach is able to self-organize a set of prototype-based IF...THEN rules from few labeled training images through an efficient supervised initialization process, and continuously self-updates the rule base with the unlabeled images in an unsupervised, autonomous, transparent and human-interpretable manner. Highly accurate classification on the unlabeled images is performed at the end of the learning process. Numerical examples demonstrate that the proposed approach is a strong alternative to the state-of-the-art ones.
Is Part Of
Subject
list of authors
presented at
volume
  • 1
  • 1
is topic of
is primary topic of
Faceted Search & Find service v1.17_git151 as of Feb 20 2025


Alternative Linked Data Documents: iSPARQL | ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 08.03.3332 as of Mar 17 2025, on Linux (x86_64-generic-linux-glibc25), Single-Server Edition (378 GB total memory, 15 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software