H
Hongmin Gao
Researcher at Hohai University
Publications - 43
Citations - 458
Hongmin Gao is an academic researcher from Hohai University. The author has contributed to research in topics: Convolutional neural network & Hyperspectral imaging. The author has an hindex of 8, co-authored 24 publications receiving 175 citations.
Papers
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Multiscale Residual Network With Mixed Depthwise Convolution for Hyperspectral Image Classification
TL;DR: A novel multiscale residual network (MSRN) is proposed for HSI classification and experimental results demonstrate the superiority of the proposed MSRN method over several state-of-the-art methods.
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Deep Belief Network for Spectral⁻Spatial Classification of Hyperspectral Remote Sensor Data.
TL;DR: Experimental results indicate that the DBN hyperspectral image classification method outperforms traditional classification and other deep learning approaches.
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An Efficient Building Extraction Method from High Spatial Resolution Remote Sensing Images Based on Improved Mask R-CNN.
TL;DR: A building extraction framework based on a convolution neural network and edge detection algorithm and called Mask R-CNN Fusion Sobel is proposed that can be applied to the recognition and segmentation of complex buildings and is superior to the classical method in accuracy.
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Multi-branch fusion network for hyperspectral image classification
TL;DR: The proposed CNN is a multi-branch fusion network, which is formed by merging multiple branches on an ordinary CNN, which can effectively extract features of HSIs and provide excellent classification performance under small training set.
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Hyperspectral Image Classification With Pre-Activation Residual Attention Network
TL;DR: A new end-to-end pre-activation residual attention network (PRAN) for HSI classification is proposed, which allows the proposed network to carry adaptively feature recalibration of channel responses and learn more robust spectral-spatial joint feature representations.