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Article

Entropy-Mediated Decision Fusion for Remotely Sensed Image Classification

School of Automation, Hangzhou Dianzi University, Hangzhou 310018, China
Remote Sens. 2019, 11(3), 352; https://doi.org/10.3390/rs11030352
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)
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
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MDPI and ACS Style

Guo, B. Entropy-Mediated Decision Fusion for Remotely Sensed Image Classification. Remote Sens. 2019, 11, 352. https://doi.org/10.3390/rs11030352

AMA Style

Guo B. Entropy-Mediated Decision Fusion for Remotely Sensed Image Classification. Remote Sensing. 2019; 11(3):352. https://doi.org/10.3390/rs11030352

Chicago/Turabian Style

Guo, Baofeng. 2019. "Entropy-Mediated Decision Fusion for Remotely Sensed Image Classification" Remote Sensing 11, no. 3: 352. https://doi.org/10.3390/rs11030352

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