Blar i forfatter Artikler, rapporter og annet (fysikk og teknologi) "Gao, Lianru"

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    • Hyperspectral image classification based on a shuffled group convolutional neural network with transfer learning 

      Liu, Yao; Gao, Lianru; Xiao, Chenchao; Qu, Ying; Zheng, Ke; Marinoni, Andrea (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-06-01)
      Convolutional neural networks (CNNs) have been widely applied in hyperspectral imagery (HSI) classification. However, their classification performance might be limited by the scarcity of labeled data to be used for training and validation. In this paper, we propose a novel lightweight shuffled group convolutional neural network (abbreviated as SG-CNN) to achieve efficient training with a limited ...

    • Hyperspectral Nonlinear Unmixing by Using Plug-and-Play Prior for Abundance Maps 

      Wang, Zhicheng; Zhuang, Lina; Gao, Lianru; Marinoni, Andrea; Zhang, Bing; Ng, Michael K. (Journal article; Tidsskriftartikkel; Peer reviewed, 2020-12-16)
      Spectral unmixing (SU) aims at decomposing the mixed pixel into basic components, called endmembers with corresponding abundance fractions. Linear mixing model (LMM) and nonlinear mixing models (NLMMs) are two main classes to solve the SU. This paper proposes a new nonlinear unmixing method base on general bilinear model, which is one of the NLMMs. Since retrieving the endmembers’ abundances represents ...

    • An improved spatial and temporal reflectance unmixing model to synthesize time series of landsat-like images 

      Ma, Jianhang; Zhang, Wenjuan; Marinoni, Andrea; Gao, Lianru; Zhang, Bing (Journal article; Tidsskriftartikkel; Peer reviewed, 2018-08-31)
      The trade-off between spatial and temporal resolution limits the acquisition of dense time series of Landsat images, and limits the ability to properly monitor land surface dynamics in time. Spatiotemporal image fusion methods provide a cost-efficient alternative to generate dense time series of Landsat-like images for applications that require both high spatial and temporal resolution images. The ...