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Yanfeng Gu
Researcher at Harbin Institute of Technology
Publications - 136
Citations - 5186
Yanfeng Gu is an academic researcher from Harbin Institute of Technology. The author has contributed to research in topics: Hyperspectral imaging & Feature extraction. The author has an hindex of 23, co-authored 108 publications receiving 3673 citations. Previous affiliations of Yanfeng Gu include University of Zurich.
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Deep Learning-Based Classification of Hyperspectral Data
TL;DR: The concept of deep learning is introduced into hyperspectral data classification for the first time, and a new way of classifying with spatial-dominated information is proposed, which is a hybrid of principle component analysis (PCA), deep learning architecture, and logistic regression.
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Deep Feature Fusion for VHR Remote Sensing Scene Classification
TL;DR: The pretrained visual geometry group network (VGG-Net) model is proposed as deep feature extractors to extract informative features from the original VHR images to produce good informative features to describe the images scene with much lower dimension.
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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).
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Multiple Kernel Learning for Hyperspectral Image Classification: A Review
TL;DR: This paper analyzes and evaluates different MKL algorithms and their respective characteristics in different cases of HSI classification cases, and discusses the future direction and trends of research in this area.
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Representative Multiple Kernel Learning for Classification in Hyperspectral Imagery
TL;DR: This paper addresses the MKL for classification in hyperspectral images by extracting the most variation from the space spanned by multiple kernels and proposes a representative MKL (RMKL) algorithm that greatly reduces the computational load for searching optimal combination of basis kernels.