LabelRS: An Automated Toolbox to Make Deep Learning Samples from Remote Sensing Images
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
*
Author to whom correspondence should be addressed.
Academic Editor: Filiberto Pla
Remote Sens. 2021, 13(11), 2064; https://doi.org/10.3390/rs13112064
Received: 26 March 2021
/
Revised: 14 May 2021
/
Accepted: 17 May 2021
/
Published: 24 May 2021
(This article belongs to the Section AI Remote Sensing)
Deep learning technology has achieved great success in the field of remote sensing processing. However, the lack of tools for making deep learning samples with remote sensing images is a problem, so researchers have to rely on a small amount of existing public data sets that may influence the learning effect. Therefore, we developed an add-in (LabelRS) based on ArcGIS to help researchers make their own deep learning samples in a simple way. In this work, we proposed a feature merging strategy that enables LabelRS to automatically adapt to both sparsely distributed and densely distributed scenarios. LabelRS solves the problem of size diversity of the targets in remote sensing images through sliding windows. We have designed and built in multiple band stretching, image resampling, and gray level transformation algorithms for LabelRS to deal with the high spectral remote sensing images. In addition, the attached geographic information helps to achieve seamless conversion between natural samples, and geographic samples. To evaluate the reliability of LabelRS, we used its three sub-tools to make semantic segmentation, object detection and image classification samples, respectively. The experimental results show that LabelRS can produce deep learning samples with remote sensing images automatically and efficiently.
View Full-Text
Keywords:
ArcGIS; deep learning; remote sensing; samples
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Li, J.; Meng, L.; Yang, B.; Tao, C.; Li, L.; Zhang, W. LabelRS: An Automated Toolbox to Make Deep Learning Samples from Remote Sensing Images. Remote Sens. 2021, 13, 2064. https://doi.org/10.3390/rs13112064
AMA Style
Li J, Meng L, Yang B, Tao C, Li L, Zhang W. LabelRS: An Automated Toolbox to Make Deep Learning Samples from Remote Sensing Images. Remote Sensing. 2021; 13(11):2064. https://doi.org/10.3390/rs13112064
Chicago/Turabian StyleLi, Junjie, Lingkui Meng, Beibei Yang, Chongxin Tao, Linyi Li, and Wen Zhang. 2021. "LabelRS: An Automated Toolbox to Make Deep Learning Samples from Remote Sensing Images" Remote Sensing 13, no. 11: 2064. https://doi.org/10.3390/rs13112064
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
