Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images

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...

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Bibliographic Details
Main Authors:	Yiming Yan, Zhichao Tan, Nan Su, Chunhui Zhao
Format:	Article
Language:	English
Published:	MDPI AG 2017-08-01
Series:	Sensors
Subjects:	stacked sparse autoencoder LIDAR DSM remote sensing image building extraction
Online Access:	https://www.mdpi.com/1424-8220/17/9/1957

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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
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