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Xiuping Jia
Researcher at University of New South Wales
Publications - 333
Citations - 11836
Xiuping Jia is an academic researcher from University of New South Wales. The author has contributed to research in topics: Hyperspectral imaging & Feature extraction. The author has an hindex of 45, co-authored 300 publications receiving 8158 citations. Previous affiliations of Xiuping Jia include Beijing Normal University & Information Technology University.
Papers
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Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks
TL;DR: This paper proposes a 3-D CNN-based FE model with combined regularization to extract effective spectral-spatial features of hyperspectral imagery and reveals that the proposed models with sparse constraints provide competitive results to state-of-the-art methods.
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Spectral–Spatial Classification of Hyperspectral Data Based on Deep Belief Network
Yushi Chen,Xing Zhao,Xiuping Jia +2 more
TL;DR: A new feature extraction (FE) and image classification framework are proposed for hyperspectral data analysis based on deep belief network (DBN) and a novel deep architecture is proposed, which combines the spectral-spatial FE and classification together to get high classification accuracy.
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Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification
Xiuping Jia,John A. Richards +1 more
TL;DR: A segmented, and possibly multistage, principal components transformation (PCT) is proposed for efficient hyperspectral remote-sensing image classification and display and results have been obtained in terms of classification accuracy, speed, and quality of color image display using two airborne visible/infrared imaging spectrometer (AVIRIS) data sets.
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Feature Mining for Hyperspectral Image Classification
TL;DR: An overview of both conventional and advanced feature reduction methods, with details on a few techniques that are commonly used for analysis of hyperspectral data.
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Nonlinear Multiple Kernel Learning With Multiple-Structure-Element Extended Morphological Profiles for Hyperspectral Image Classification
TL;DR: A novel multiple kernel learning (MKL) framework to incorporate both spectral and spatial features for hyperspectral image classification, which is called multiple-structure-element nonlinear MKL (MultiSE-NMKL).