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Journal of Applied Remote Sensing • new

Collaborative classification of hyperspectral and visible images with convolutional neural network
Author(s): Mengmeng Zhang; Wei Li; Qian Du
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Paper Abstract

Recent advances in remote sensing technology have made multisensor data available for the same area, and it is well-known that remote sensing data processing and analysis often benefit from multisource data fusion. Specifically, low spatial resolution of hyperspectral imagery (HSI) degrades the quality of the subsequent classification task while using visible (VIS) images with high spatial resolution enables high-fidelity spatial analysis. A collaborative classification framework is proposed to fuse HSI and VIS images for finer classification. First, the convolutional neural network model is employed to extract deep spectral features for HSI classification. Second, effective binarized statistical image features are learned as contextual basis vectors for the high-resolution VIS image, followed by a classifier. The proposed approach employs diversified data in a decision fusion, leading to an integration of the rich spectral information, spatial information, and statistical representation information. In particular, the proposed approach eliminates the potential problems of the curse of dimensionality and excessive computation time. The experiments evaluated on two standard data sets demonstrate better classification performance offered by this framework.

Paper Details

Date Published: 1 September 2017
PDF: 12 pages
J. Appl. Rem. Sens. 11(4) 042607 doi: 10.1117/1.JRS.11.042607
Published in: Journal of Applied Remote Sensing Volume 11, Issue 4
Show Author Affiliations
Mengmeng Zhang, Beijing Univ. of Chemical Technology (China)
Wei Li, Beijing Univ. of Chemical Technology (China)
Qian Du, Mississippi State Univ. (United States)

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