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

• Published in: Remote sensing (Basel, Switzerland) Vol. 12; no. 20; p. 3421
• Main Authors: Liu, Tiange, Miao, Qiguang, Xu, Pengfei, Zhang, Shihui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 18.10.2020
• Subjects: Accuracy; area; Artificial neural networks; Boundary maps; chemical elements; Clustering; Color; data collection; extraction; information; Information processing; Information retrieval; Methods; Neighborhoods; Neural networks; precision; Remote sensing; scanned topographic map; Segmentation; shallow convolutional neural network; superpixel; testing; Topographic mapping; Topographic maps; Topography; watershed transform; Watersheds
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs12203421

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.