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Danfeng Hong
Researcher at German Aerospace Center
Publications - 153
Citations - 8220
Danfeng Hong is an academic researcher from German Aerospace Center. The author has contributed to research in topics: Computer science & Hyperspectral imaging. The author has an hindex of 26, co-authored 88 publications receiving 3023 citations. Previous affiliations of Danfeng Hong include Qingdao University & Technische Universität München.
Papers
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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).
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Cascaded Recurrent Neural Networks for Hyperspectral Image Classification
TL;DR: Wang et al. as discussed by the authors proposed a sequence-based recurrent neural network (RNN) for hyperspectral image classification, which makes use of a newly proposed activation function, parametric rectified tanh (PRetanh), instead of the popular tanh or rectified linear unit.
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Graph Convolutional Networks for Hyperspectral Image Classification
TL;DR: A new minibatch GCN is developed that is capable of inferring out-of-sample data without retraining networks and improving classification performance, and three fusion strategies are explored: additive fusion, elementwise multiplicative fusion, and concatenation fusion to measure the obtained performance gain.
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Graph Convolutional Networks for Hyperspectral Image Classification
TL;DR: In this paper, a mini-batch graph convolutional network (called miniGCN) is proposed for hyperspectral image classification, which allows to train large-scale GCNs in a minibatch fashion.
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An Augmented Linear Mixing Model to Address Spectral Variability for Hyperspectral Unmixing
TL;DR: This work proposes a novel spectral mixture model, called the augmented LMM, to address spectral variability by applying a data-driven learning strategy in inverse problems of hyperspectral unmixing and introduces a spectral variability dictionary.