Next Article in Journal
Maritime Vessel Classification to Monitor Fisheries with SAR: Demonstration in the North Sea
Next Article in Special Issue
MultiCAM: Multiple Class Activation Mapping for Aircraft Recognition in Remote Sensing Images
Previous Article in Journal
High-Resolution Mapping of Redwood (Sequoia sempervirens) Distributions in Three Californian Forests
Previous Article in Special Issue
Superpixel-Guided Layer-Wise Embedding CNN for Remote Sensing Image Classification
Article Menu
Issue 3 (February-1) cover image

Export Article

Open AccessArticle
Remote Sens. 2019, 11(3), 352; https://doi.org/10.3390/rs11030352

Entropy-Mediated Decision Fusion for Remotely Sensed Image Classification

School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
Received: 26 January 2019 / Accepted: 6 February 2019 / Published: 10 February 2019
(This article belongs to the Special Issue Pattern Analysis and Recognition in Remote Sensing)
Full-Text   |   PDF [866 KB, uploaded 13 February 2019]   |  
  |   Review Reports

Abstract

To better classify remotely sensed hyperspectral imagery, we study hyperspectral signatures from a different view, in which the discriminatory information is divided as reflectance features and absorption features, respectively. Based on this categorization, we put forward an information fusion approach, where the reflectance features and the absorption features are processed by different algorithms. Their outputs are considered as initial decisions, and then fused by a decision-level algorithm, where the entropy of the classification output is used to balance between the two decisions. The final decision is reached by modifying the decision of the reflectance features via the results of the absorption features. Simulations are carried out to assess the classification performance based on two AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) hyperspectral datasets. The results show that the proposed method increases the classification accuracy against the state-of-the-art methods. View Full-Text
Keywords: hyperspectral image; classification; decision-level fusion; multi-view learning hyperspectral image; classification; decision-level fusion; multi-view learning
Figures

Graphical abstract

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Guo, B. Entropy-Mediated Decision Fusion for Remotely Sensed Image Classification. Remote Sens. 2019, 11, 352.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top