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21 results on '"Li Wenmei"'

3. Characterizing Markov Random Fields and Coefficient of Variations as Measures of Spatial Distributions for Hyperspectral Image Classification.

Author

Cui, Bin, Peng, Yao, Zhang, Hao, Li, Wenmei, and Du, Peijun

Abstract

Characterizing spatial information as reinforcement of spectral signatures can largely assist the performance in hyperspectral image (HSI) classification. Markov random fields (MRFs) are probabilistic image texture models and are capable of encoding contextual dependencies through characterizing local conditional probabilities. As a representative standardized measure of dispersion of image probability distributions, the coefficient of variation (CoV) can be a useful tool for characterizing spatial heterogeneity. Their parameter derivation processes also share strong compatibility with convolutional neural networks (CNNs) that specify spatial correlations in local neighborhoods. In this work, we propose an MRF and CoV-based spectral–spatial convolutional network (MRF-CoV-CNN) for HSI classification. MRF models and CoVs are characterized as measures of spatial distributions and further combined with spectral information. Then the proposed MRF-CoV-CNN takes the fused features as input and produces reliable classification results. Comprehensive experiments have been conducted on the Pavia University dataset and the Salinas dataset to evaluate the proposed method both visually and quantitatively. [ABSTRACT FROM AUTHOR]

Published
2023
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14. Feasibility analysis of hemi-boreal forest biomass estimation using Tomo-SAR

Author

Li Wenmei, Zhang Wangfei, Li Zengyuan, and Chen Erxue

Subjects
Above ground, Correlation coefficient, Test site, Taiga, Biomass, Environmental science, Atmospheric sciences, Remote sensing
Abstract

The aim of this paper is to analyze the feasibility of hemi-boreal forest above ground biomass (AGB) estimation based on Tomo-SAR technique. Repeat-path multi-baseline P-band Pol-InSAR data collected during March and May, 2007 in Remningstorp test site is used. The result shows that the correlation coefficient (R) of P-band HH backscattering coefficient reaches 0.87 with the in-situ forest biomass. The R of P-band VV backscattering power at 5m and 10m is 0.71, 0.72 with the in-situ forest biomass, respectively.

Published
2014
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21. A fidelity simulator for forest region based on actual working process of SAR remote sensing.

Author

Sun, Hanwei, Hu, Cheng, Zeng, Tao, Long, Teng, Li, Wenmei, and Chen, Erxue

Abstract

Synthetic Aperture Radar (SAR) simulation of forest region is a powerful tool for understanding the mechanism of remote sensing, testing new applications and devising inversion algorithms of forest structure. The common approaches used to simulate images of forest region are lack of the ability to be faithful to the actual characteristics of the remote sensing systems. This paper presents a fidelity simulator for forest region based on actual working process of SAR remote sensing to meet the nonideal features in actual conditions. The simulator considers raw echoes generating and image focusing process with various kinds of uncertain factors including trajectory deviations, attitude dithering, calibration error, noises. The simulator is verified by comparing with E-SAR data via Polarization Coherence Tomography (PCT) process. The results indicate the simulated images have the similar features with E-SAR images. Further applications of the simulator are also discussed and take simulation of trajectory deviation as an application example. [ABSTRACT FROM PUBLISHER]

Published
2012
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