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Sensors 2016, 16(12), 2146; doi:10.3390/s16122146

Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification

School of Electronic, Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Author to whom correspondence should be addressed.
Academic Editors: Cheng Wang, Julian Smit, Ayman F. Habib and Michael Ying Yang
Received: 13 November 2016 / Revised: 6 December 2016 / Accepted: 12 December 2016 / Published: 16 December 2016
(This article belongs to the Special Issue Multi-Sensor Integration and Fusion)
View Full-Text   |   Download PDF [9390 KB, uploaded 16 December 2016]   |  

Abstract

Classification is a significant subject in hyperspectral remote sensing image processing. This study proposes a spectral-spatial feature fusion algorithm for the classification of hyperspectral images (HSI). Unlike existing spectral-spatial classification methods, the influences and interactions of the surroundings on each measured pixel were taken into consideration in this paper. Data field theory was employed as the mathematical realization of the field theory concept in physics, and both the spectral and spatial domains of HSI were considered as data fields. Therefore, the inherent dependency of interacting pixels was modeled. Using data field modeling, spatial and spectral features were transformed into a unified radiation form and further fused into a new feature by using a linear model. In contrast to the current spectral-spatial classification methods, which usually simply stack spectral and spatial features together, the proposed method builds the inner connection between the spectral and spatial features, and explores the hidden information that contributed to classification. Therefore, new information is included for classification. The final classification result was obtained using a random forest (RF) classifier. The proposed method was tested with the University of Pavia and Indian Pines, two well-known standard hyperspectral datasets. The experimental results demonstrate that the proposed method has higher classification accuracies than those obtained by the traditional approaches. View Full-Text
Keywords: feature fusion; data field theory; hyperspectral data; mathematical morphology; spectral-spatial classification feature fusion; data field theory; hyperspectral data; mathematical morphology; spectral-spatial classification
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Liu, D.; Li, J. Data Field Modeling and Spectral-Spatial Feature Fusion for Hyperspectral Data Classification. Sensors 2016, 16, 2146.

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