1 September 2017 Collaborative classification of hyperspectral and visible images with convolutional neural network
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J. of Applied Remote Sensing, 11(4), 042607 (2017). doi:10.1117/1.JRS.11.042607
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.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE)
Mengmeng Zhang, Wei Li, Qian Du, "Collaborative classification of hyperspectral and visible images with convolutional neural network," Journal of Applied Remote Sensing 11(4), 042607 (1 September 2017). http://dx.doi.org/10.1117/1.JRS.11.042607 Submission: Received 21 April 2017; Accepted 3 August 2017
Submission: Received 21 April 2017; Accepted 3 August 2017
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KEYWORDS
Image classification

Image segmentation

Image fusion

Hyperspectral imaging

Spatial resolution

Lithium

Information fusion

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