Next Article in Journal
Retrieval and Validation of AOD from Himawari-8 Data over Bohai Rim Region, China
Previous Article in Journal
A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the ‘Cipolla Rossa di Tropea’ (Italy)
Open AccessArticle

Superpixel-Based Shallow Convolutional Neural Network (SSCNN) for Scanned Topographic Map Segmentation

1
School of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, China
2
School of Computer Science and Technology, Xidian University, Xi’an 710126, China
3
Information Science and Technology School, Northwest University, Xi’an 710127, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(20), 3421; https://doi.org/10.3390/rs12203421
Received: 5 September 2020 / Revised: 12 October 2020 / Accepted: 16 October 2020 / Published: 18 October 2020
(This article belongs to the Section Remote Sensing Image Processing)
Motivated by applications in topographic map information extraction, our goal was to discover a practical method for scanned topographic map (STM) segmentation. We present an advanced guided watershed transform (AGWT) to generate superpixels on STM. AGWT utilizes the information from both linear and area elements to modify detected boundary maps and sequentially achieve superpixels based on the watershed transform. With achieving an average of 0.06 on under-segmentation error, 0.96 on boundary recall, and 0.95 on boundary precision, it has been proven to have strong ability in boundary adherence, with fewer over-segmentation issues. Based on AGWT, a benchmark for STM segmentation based on superpixels and a shallow convolutional neural network (SCNN), termed SSCNN, is proposed. There are several notable ideas behind the proposed approach. Superpixels are employed to overcome the false color and color aliasing problems that exist in STMs. The unification method of random selection facilitates sufficient training data with little manual labeling while keeping the potential color information of each geographic element. Moreover, with the small number of parameters, SCNN can accurately and efficiently classify those unified pixel sequences. The experiments show that SSCNN achieves an overall F1 score of 0.73 on our STM testing dataset. They also show the quality of the segmentation results and the short run time of this approach, which makes it applicable to full-size maps. View Full-Text
Keywords: scanned topographic map; segmentation; superpixel; shallow convolutional neural network; watershed transform scanned topographic map; segmentation; superpixel; shallow convolutional neural network; watershed transform
Show Figures

Graphical abstract

  • Externally hosted supplementary file 1
    Link: https://github.com/wolfman623/SSCNN.git
    Description: In this Github repository, we provide five map patches (numbered map1 to map5 in our manuscript) as well as the manually labeled ground truths, which you may find them in ./dataset/, to test the performance of our method.
MDPI and ACS Style

Liu, T.; Miao, Q.; Xu, P.; Zhang, S. Superpixel-Based Shallow Convolutional Neural Network (SSCNN) for Scanned Topographic Map Segmentation. Remote Sens. 2020, 12, 3421.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop