P
Peng Zeng
Publications - 5
Citations - 14
Peng Zeng is an academic researcher. The author has contributed to research in topics: Computer science & Polarimetry. The author has an hindex of 2, co-authored 5 publications receiving 14 citations.
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Journal ArticleDOI
A Combined Convolutional Neural Network for Urban Land-Use Classification with GIS Data
TL;DR: The proposed DUA-Net method proved suitable for high-precision urban land-use classification, which will be of great value for urban planning and national land resource surveying.
Journal ArticleDOI
Forest Total and Component Above-Ground Biomass (AGB) Estimation through C- and L-band Polarimetric SAR Data
TL;DR: In this article , the backscattering coefficients at different polarimetric channels were extracted from Freeman2, Yamaguchi3, H-A-Alpha, and Target Scattering Vector Model (TSVM) decomposition methods to estimate the total forest AGB and biomass components of two test sites in China.
Journal ArticleDOI
Forest total and component biomass retrieval via GA-SVR algorithm and quad-polarimetric SAR data
TL;DR: In this paper , the authors used quad-polarimetric SAR data at C- and L- bands, extracting the backscatter coefficients and polarimetric features derived from four polarization decomposition methods (Yamaguchi 3-component decomposition, Freeman 2-component decomposition, H/A/alpha decomposition and TSVM decomposition) as the input to the GA-SVR for forest component AGB estimation.
Proceedings ArticleDOI
Component Forest Above Ground Biomass Estimation Using Lidar and Sardata
TL;DR: In this article , LiDAR and X-band Synthetic Aperture Radar (SAR) data were used in separately and in combination to estimate total forest above-ground biomass (AGB), but rarely used in components AGB estimation.
Journal ArticleDOI
Exploiting Hierarchical Label Information in an Attention-Embedding, Multi-Task, Multi-Grained, Network for Scene Classification of Remote Sensing Imagery
TL;DR: The results indicate that hierarchical label information can effectively improve the performance of scene classification tasks when categorizing remote sensing imagery.