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

Yan, Yiming; Tan, Zhichao; Su, Nan; Zhao, Chunhui

doi:10.3390/s19010131

Open AccessCorrection

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

by

Yiming Yan

^*,

Zhichao Tan

^*,

Nan Su

^* and

Chunhui Zhao

College of Information and Communication Engineering, Harbin Engineering University, Harbin 150001, China

^*

Authors to whom correspondence should be addressed.

Sensors 2019, 19(1), 131; https://doi.org/10.3390/s19010131

Submission received: 20 December 2018 / Accepted: 27 December 2018 / Published: 2 January 2019

(This article belongs to the Section Remote Sensors)

Download Versions Notes

The authors wish to make the following corrections to this paper [1]:

Changes in Acknowledgement

Due to a lapse, some important contents were missed in the Acknowledgement section from the original article [1].

[Acknowledgments: The authors would like to thank the support by the Fund of the National Natural Science Foundation of China under Grant No. 61601135 and Natural Science Foundation of Heilongjiang Province of China under Grant No. QC201706802. Additionally, the Vaihingen data set was provided by the German Society for Photogrammetry, Remote Sensing and Geoinformation (DGPF) [Cramer, 2010]: http://www.ifp.uni-stuttgart.de/dgpf/DKEPAllg.html. The experimental SVM program comes from libsvm-3.1-[FarutoUltimate3.1Mcode]. Faruto and liyang, LIBSVM-faruto Ultimate Version, a toolbox with implements for support vector machines were based on libsvm, 2011. Software is available at http://www.matlabsky.com. Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines, 2001. Software is available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. The experimental SAE program comes from the rasmusbergpalm-DeepLearnToolbox-9faf641 toolkit. Software is available at https://github.com/rasmusbergpalm/DeepLearnToolbox.]

The authors would like to apologize for any inconvenience caused to the readers by these changes.

Reference

Yan, Y.; Tan, Z.; Su, N.; Zhao, C. Building Extraction Based on an Optimized Stacked Sparse Autoencoder of Structure and Training Samples Using LIDAR DSM and Optical Images. Sensors 2017, 17, 1957. [Google Scholar] [CrossRef] [PubMed]

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

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Yan, Y.; Tan, Z.; Su, N.; Zhao, C. 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. Sensors 2019, 19, 131. https://doi.org/10.3390/s19010131

AMA Style

Yan Y, Tan Z, Su N, Zhao C. 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. Sensors. 2019; 19(1):131. https://doi.org/10.3390/s19010131

Chicago/Turabian Style

Yan, Yiming, Zhichao Tan, Nan Su, and Chunhui Zhao. 2019. "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" Sensors 19, no. 1: 131. https://doi.org/10.3390/s19010131

APA Style

Yan, Y., Tan, Z., Su, N., & Zhao, C. (2019). 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. Sensors, 19(1), 131. https://doi.org/10.3390/s19010131

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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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