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Article

A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance

by 1, 1,2,* and 1
1
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
2
Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1954; https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161954
Received: 16 June 2019 / Revised: 9 August 2019 / Accepted: 18 August 2019 / Published: 20 August 2019
(This article belongs to the Special Issue Advanced Machine Learning Approaches for Hyperspectral Data Analysis)
Today, more and more deep learning frameworks are being applied to hyperspectral image classification tasks and have achieved great results. However, such approaches are still hampered by long training times. Traditional spectral–spatial hyperspectral image classification only utilizes spectral features at the pixel level, without considering the correlation between local spectral signatures. Our article has tested a novel hyperspectral image classification pattern, using random-patches convolution and local covariance (RPCC). The RPCC is an effective two-branch method that, on the one hand, obtains a specified number of convolution kernels from the image space through a random strategy and, on the other hand, constructs a covariance matrix between different spectral bands by clustering local neighboring pixels. In our method, the spatial features come from multi-scale and multi-level convolutional layers. The spectral features represent the correlations between different bands. We use the support vector machine as well as spectral and spatial fusion matrices to obtain classification results. Through experiments, RPCC is tested with five excellent methods on three public data-sets. Quantitative and qualitative evaluation indicators indicate that the accuracy of our RPCC method can match or exceed the current state-of-the-art methods. View Full-Text
Keywords: random patches convolution; local covariance; feature extraction; hyperspectral image classification random patches convolution; local covariance; feature extraction; hyperspectral image classification
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MDPI and ACS Style

Sun, Y.; Fu, Z.; Fan, L. A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance. Remote Sens. 2019, 11, 1954. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161954

AMA Style

Sun Y, Fu Z, Fan L. A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance. Remote Sensing. 2019; 11(16):1954. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161954

Chicago/Turabian Style

Sun, Yangjie, Zhongliang Fu, and Liang Fan. 2019. "A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance" Remote Sensing 11, no. 16: 1954. https://0-doi-org.brum.beds.ac.uk/10.3390/rs11161954

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