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- W2969508585 startingPage "1954" @default.
- W2969508585 abstract "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." @default.
- W2969508585 created "2019-08-29" @default.
- W2969508585 creator A5003175887 @default.
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- W2969508585 date "2019-08-20" @default.
- W2969508585 modified "2023-10-03" @default.
- W2969508585 title "A Novel Hyperspectral Image Classification Pattern Using Random Patches Convolution and Local Covariance" @default.
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- W2969508585 doi "https://doi.org/10.3390/rs11161954" @default.
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