Prof. Zhonghua Hong Profile

Prof. Zhonghua Hong

Prof. at Shanghai Ocean University

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Publications (5)

SPIE Journal Paper | 27 September 2024 Open Access

DB-Net: dual-branch deep learning network for cloud detection utilizing attention mechanism and cascaded multiscale feature fusion

Ruyan Zhou, Chenhao Zhang, Haiyan Pan, Haiyang He, Yun Zhang, Yanling Han, Jing Wang, Shuhu Yang, Zhonghua Hong

JARS, Vol. 18, Issue 03, 034524, (September 2024) https://doi.org/10.1117/12.10.1117/1.JRS.18.034524

KEYWORDS: Clouds, Feature extraction, Transformers, Deep learning, Convolution, Semantics, Finite element methods, Feature fusion, Satellites, Remote sensing

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SPIE Journal Paper | 26 November 2022

Remote sensing sea ice classification based on DenseNet and heterogeneous data fusion

Yanling Han, Hang Shen, Zhonghua Hong, Yun Zhang, Haiyan Pan, Ruyan Zhou, Jing Wang

JARS, Vol. 16, Issue 04, 044517, (November 2022) https://doi.org/10.1117/12.10.1117/1.JRS.16.044517

KEYWORDS: Ice, Synthetic aperture radar, Data fusion, Remote sensing, Data modeling, Feature extraction, Image fusion, Ocean optics, Image classification, Convolution

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Most remote sensing sea ice classification methods use single-source remote sensing data, such as synthetic aperture radar (SAR) data and optical remote sensing data. SAR data contain rich sea ice texture information, but the data are relatively single, making it difficult to distinguish detailed sea ice categories. Optical data include abundant spatial-spectral information, but they are often affected by clouds, fog, and severe weather. Hence, given the limitations of single-source data, the remote sensing sea ice classification accuracy cannot be further improved. A sea ice classification method based on deep learning and multisource remote sensing data fusion is proposed utilizing an improved densely connected convolutional neural network (DenseNet) to mine and fuse the multilevel features of sea ice. According to the characteristics of SAR data and optical data, a dual-branch network structure based on an improved DenseNet is employed for feature extraction, and a squeeze-and-excitation attention mechanism is introduced to weight the fusion features to further enhance the feature weights that can effectively distinguish different types of sea ice. The fully connected network is used to perform the deep fusion of features and classify sea ice. To verify the effectiveness of the proposed method, two sets of sea ice data are utilized for sea ice classification. The experimental results show that the proposed method fully excavates and fuses the multilevel characteristics of heterogeneous data using the improved dual-branch network structure, leverages the complementary characteristics of SAR data and optical data, significantly increases the sea ice classification accuracy, and effectively improves the influence of cloud cover on the sea ice classification accuracy. Compared with the typical single-source data classification method and other heterogeneous data fusion classification methods, the proposed method achieves superior overall classification accuracy (98.49% and 98.58%).

SPIE Journal Paper | 15 May 2018

Combining active learning and transductive support vector machines for sea ice detection

Yanling Han, Peng Li, Yun Zhang, Zhonghua Hong, Kaichen Liu, Jing Wang

JARS, Vol. 12, Issue 02, 026016, (May 2018) https://doi.org/10.1117/12.10.1117/1.JRS.12.026016

KEYWORDS: Solid state lighting, Image classification, Earth observing sensors, Remote sensing, Landsat, Statistical modeling, Binary data, Environmental sensing, Spatial resolution, Image processing

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SPIE Journal Paper | 14 October 2015

Evaluation of Galileo navigation system positioning performance in Shanghai

Yun Zhang, Zhonghua Hong, Yanling Han, Lijun Xu, Yushi Song, Kun Zhang

JARS, Vol. 9, Issue 01, 096001, (October 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.096001

KEYWORDS: Satellites, Global Positioning System, Signal to noise ratio, Error analysis, Navigation systems, Receivers, Buildings, Satellite navigation systems, Antennas, Phase measurement

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SPIE Journal Paper | 12 May 2015

Rapid three-dimensional detection approach for building damage due to earthquakes by the use of parallel processing of unmanned aerial vehicle imagery

Zhonghua Hong, Xiaohua Tong, Wen Cao, Shenglu Jiang, Peng Chen, Shijie Liu

JARS, Vol. 9, Issue 01, 097292, (May 2015) https://doi.org/10.1117/12.10.1117/1.JRS.9.097292

KEYWORDS: Unmanned aerial vehicles, Earthquakes, 3D image processing, Parallel processing, Damage detection, Cameras, Image processing, Clouds, 3D modeling, Visualization

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