Cargando…
Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images
In this paper, a building extraction method is proposed based on a stacked sparse autoencoder with an optimized structure and training samples. Building extraction plays an important role in urban construction and planning. However, some negative effects will reduce the accuracy of extraction, such...
Autores principales: | Yan, Yiming, Tan, Zhichao, Su, Nan, Zhao, Chunhui |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5621110/ https://www.ncbi.nlm.nih.gov/pubmed/28837118 http://dx.doi.org/10.3390/s17091957 |
Ejemplares similares
-
Correction: Yan, Y.; et al. Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images. Sensors 2017, 17, 1957
por: Yan, Yiming, et al.
Publicado: (2019) -
Classification of Thyroid Nodules with Stacked Denoising Sparse Autoencoder
por: Li, Zexin, et al.
Publicado: (2020) -
Optimal Deep Stacked Sparse Autoencoder Based Osteosarcoma Detection and Classification Model
por: Fakieh, Bahjat, et al.
Publicado: (2022) -
SARS-CoV-2 virus classification based on stacked sparse autoencoder
por: Coutinho, Maria G.F., et al.
Publicado: (2022) -
Automatic Sleep Stage Scoring Using Time-Frequency Analysis and Stacked Sparse Autoencoders
por: Tsinalis, Orestis, et al.
Publicado: (2015)