Q
Qian Du
Researcher at Mississippi State University
Publications - 621
Citations - 27841
Qian Du is an academic researcher from Mississippi State University. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 62, co-authored 555 publications receiving 18872 citations. Previous affiliations of Qian Du include University of Maryland, Baltimore County & Texas A&M University–Kingsville.
Papers
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Journal ArticleDOI
Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Jose M. Bioucas-Dias,Antonio Plaza,Nicolas Dobigeon,Mario Parente,Qian Du,Paul D. Gader,Jocelyn Chanussot +6 more
TL;DR: This paper presents an overview of un Mixing methods from the time of Keshava and Mustard's unmixing tutorial to the present, including Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixed algorithms.
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Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches
Jose M. Bioucas-Dias,Antonio Plaza,Nicolas Dobigeon,Mario Parente,Qian Du,Paul D. Gader,Jocelyn Chanussot +6 more
TL;DR: An overview of unmixing methods from the time of Keshava and Mustard's tutorial as mentioned in this paper to the present can be found in Section 2.2.1].
Journal ArticleDOI
Estimation of number of spectrally distinct signal sources in hyperspectral imagery
Chein-I Chang,Qian Du +1 more
TL;DR: A new definition of virtual dimensionality (VD) is introduced, defined as the minimum number of spectrally distinct signal sources that characterize the hyperspectral data from the perspective view of target detection and classification.
Journal ArticleDOI
Hyperspectral Image Classification Using Deep Pixel-Pair Features
TL;DR: Experimental results based on several hyperspectral image data sets demonstrate that the proposed pixel-pair method can achieve better classification performance than the conventional deep learning-based method.
Journal ArticleDOI
More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification
TL;DR: A baseline solution to the aforementioned difficulty by developing a general multimodal deep learning (MDL) framework that is not only limited to pixel-wise classification tasks but also applicable to spatial information modeling with convolutional neural networks (CNNs).