L
Lianru Gao
Researcher at Chinese Academy of Sciences
Publications - 234
Citations - 6834
Lianru Gao is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Hyperspectral imaging & Computer science. The author has an hindex of 27, co-authored 200 publications receiving 2931 citations. Previous affiliations of Lianru Gao include Shenzhen University & China University of Mining and Technology.
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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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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Multisource Remote Sensing Data Classification Based on Convolutional Neural Network
TL;DR: The classification fusion of hyperspectral imagery (HSI) and data from other multiple sensors, such as light detection and ranging (LiDAR) data, is investigated with the state-of-the-art deep learning, named the two-branch convolution neural network (CNN).
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Feature Extraction for Classification of Hyperspectral and LiDAR Data Using Patch-to-Patch CNN
TL;DR: An unsupervised feature extraction framework, named as patch-to-patch convolutional neural network (PToP CNN), is proposed for collaborative classification of hyperspectral and LiDAR data and provides superior performance when compared with some state-of-the-art classifiers, such as two-branch CNN and context CNN.