Junmin Meng
State Oceanic Administration
72 Papers
88 Citations
Junmin Meng is an academic researcher from State Oceanic Administration. The author has contributed to research in topics: Synthetic aperture radar & Computer science. The author has an hindex of 13, co-authored 40 publications. Previous affiliations of Junmin Meng include China Aerospace Science and Technology Corporation.
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Papers
Ship Classification in SAR Image by Joint Feature and Classifier Selection
TL;DR: A joint feature and classifiers selection method is proposed by integrating the classifier selection strategy into a wrapper feature selection framework and the sequential forward floating searching algorithm is improved to conduct efficient searching for an optimal triplet of feature-scaling-classifier.
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Ship Classification Based on Superstructure Scattering Features in SAR Images
TL;DR: A novel method that uses synthetic-aperture-radar images to distinguish ships based on superstructure scattering features and employs peak extraction to divide a ship into bow, middle, and stern instead of into three equal parts can achieve satisfactory ship-classification performance compared with existing methods.
75
Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier–Feature Assembly
TL;DR: RF with feature combination of TeW, LeW, Sigma0, MAX, and PP was finally selected as the OCF for sea ice classification and the results that demonstrated this method achieved a mean accuracy of 91.45%, which outperformed the other state-of-art methods by 9%.
56
A wave energy resource assessment in the China’s seas based on multi-satellite merged radar altimeter data
TL;DR: In this paper, a wave power density model that considers the effects of the water depth is introduced to improve the calculating accuracy of the wave power densities in both offshore and nearshore areas of China's seas.
46
PCA-based sea-ice image fusion of optical data by HIS transform and SAR data by wavelet transform
TL;DR: It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.
44