Cargando…
Fusion of Hyperspectral CASI and Airborne LiDAR Data for Ground Object Classification through Residual Network
Modern satellite and aerial imagery outcomes exhibit increasingly complex types of ground objects with continuous developments and changes in land resources. Single remote-sensing modality is not sufficient for the accurate and satisfactory extraction and classification of ground objects. Hyperspect...
Autores principales: | Chang, Zhanyuan, Yu, Huiling, Zhang, Yizhuo, Wang, Keqi |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7412085/ https://www.ncbi.nlm.nih.gov/pubmed/32708693 http://dx.doi.org/10.3390/s20143961 |
Ejemplares similares
-
Evaluation of hyperspectral LiDAR for monitoring rice leaf nitrogen by comparison with multispectral LiDAR and passive spectrometer
por: Sun, Jia, et al.
Publicado: (2017) -
Estimating above-ground biomass of subtropical forest using airborne LiDAR in Hong Kong
por: Chan, Evian Pui Yan, et al.
Publicado: (2021) -
Dual-Coupled CNN-GCN-Based Classification for Hyperspectral and LiDAR Data
por: Wang, Lei, et al.
Publicado: (2022) -
Feasibility Study on the Classification of Persimmon Trees’ Components Based on Hyperspectral LiDAR
por: Shao, Hui, et al.
Publicado: (2023) -
Optimal LiDAR Data Resolution Analysis for Object Classification
por: Darrah, Marjorie, et al.
Publicado: (2022)