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Open AccessFeature PaperArticle

Pattern Recognition and Segmentation of Administrative Boundaries Using a One-Dimensional Convolutional Neural Network and Grid Shape Context Descriptor

by Min Yang, Haoran Huang, Yiqi Zhang and Xiongfeng Yan

ISPRS Int. J. Geo-Inf. 2022, 11(9), 461; https://doi.org/10.3390/ijgi11090461 - 28 Aug 2022

Cited by 8 | Viewed by 3346

Abstract

Recognizing morphological patterns in lines and segmenting them into homogeneous segments is critical for line generalization and other applications. Due to the excessive dependence on handcrafted features in existing methods and their insufficient consideration of contextual information, we propose a novel pattern recognition and segmentation method for lines, based on deep learning and shape context descriptors. In this method, a line is divided into a series of consecutive linear units of equal length, termed lixels. A grid shape context descriptor (GSCD) was designed to extract the contextual features for each lixel. A one-dimensional convolutional neural network (1D-U-Net) was constructed to classify the pattern type of each lixel, and adjacent lixels with the same pattern types were fused to obtain segmentation results. The proposed method was applied to administrative boundaries, which were segmented into components with three different patterns. The experiments showed that the lixel classification accuracy of the 1D-U-Net reached 90.42%. The consistency ratio was 92.41%, when compared with the manual segmentation results, which was higher than either of the two existing machine learning-based segmentation methods. Full article

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21 pages, 17977 KB

Open AccessArticle

Towards Measuring Shape Similarity of Polygons Based on Multiscale Features and Grid Context Descriptors

by Hongchao Fan, Zhiyao Zhao and Wenwen Li

ISPRS Int. J. Geo-Inf. 2021, 10(5), 279; https://doi.org/10.3390/ijgi10050279 - 28 Apr 2021

Cited by 15 | Viewed by 6065

Abstract

In spatial analysis applications, measuring the shape similarity of polygons is crucial for polygonal object retrieval and shape clustering. As a complex cognition process, measuring shape similarity should involve finding the difference between polygons, as objects in observation, in terms of visual perception and the differences of the regions, boundaries, and structures formed by the polygons from a mathematical point of view. In existing approaches, the shape similarity of polygons is calculated by only comparing their mathematical characteristics while not taking human perception into consideration. Aiming to solve this problem, we use the features of context and texture of polygons, since they are basic visual perception elements, to fit the cognition purpose. In this paper, we propose a contour diffusion method for the similarity measurement of polygons. By converting a polygon into a grid representation, the contour feature is represented as a multiscale statistic feature, and the region feature is transformed into condensed grid of context features. Instead of treating shape similarity as a distance between two representations of polygons, the proposed method observes similarity as a correlation between textures extracted by shape features. The experiments show that the accuracy of the proposed method is superior to that of the turning function and Fourier descriptor. Full article

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