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Adaptive scale selection in multiscale segmentation based on the segmented object complexity of GF-2 satellite image

  • GMGDA 2019
  • Published:
Arabian Journal of Geosciences Aims and scope Submit manuscript

Abstract

Image segmentation is the premise and key step of object-oriented classification, but scale selection remains a challenge in image segmentation. Over the years, scale selection methods of image segmentation have been extensively explored and developed. When the scale is chosen, all the features are generally extracted from images in these methods. In addition, in these methods, an optimal scale is generally selected based on the pre-estimation of the statistical variance of remote sensing images or determined by the post-segmentation evaluation results, rather than during the segmentation. In this study, with the central and eastern parts of Longchang City in Mid-Sichuan Hilly Region as the study area, based on the Gaofen-2 (GF-2) image, an adaptive scale selection method was proposed to determine the optimal scale of each segmentation object during the segmentation. First, the single-scale optimal image segmentation is determined by an unsupervised evaluation method which uses weighted variance and Moran’s I to measure global intra-segment homogeneity and inter-segment heterogeneity, respectively. Then, spectral, texture, and shape features of each segmented object were extracted to construct the complexity index function, and the optimal scale of the segmentation objects was determined through the iterative calculation according to the threshold value. The proposed method was applied to process GF-2 image to obtain the segmentation results and classification map, compared with the methods of determining the optimal scale through estimation local statistic variances of image and post-evaluation of segmentation results. The experimental results showed that the proposed method is of practical helpfulness and effectiveness for generating the optimal scale matching the actual ground objects.

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Acknowledgments

We appreciate CRESDA for providing the GF-2 image data and acknowledge the careful guidance of Professor Wunian Yang and helpful suggestions of the members of the research group.

Funding

This study received financial support from the National Natural Science Foundation Project of China (Granted Nos. 41671432 and 41372340) and Department of Land and Resources Project of Sichuan Province of China (KJ-2016-12).

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Authors and Affiliations

  1. Key Laboratory of Geoscience Spatial Information Technology of Ministry of Land and Resources, Chengdu University of Technology, Chengdu, 610059, China

    Fang Wang, Wunian Yang & Jintong Ren

  2. College of Geography and Resources Science, Neijiang Normal University, Neijiang, 641100, China

    Fang Wang

Authors
  1. Fang Wang
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  2. Wunian Yang
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  3. Jintong Ren
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Correspondence to Wunian Yang.

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This article is part of the Topical Collection on Geological Modeling and Geospatial Data Analysis

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Wang, F., Yang, W. & Ren, J. Adaptive scale selection in multiscale segmentation based on the segmented object complexity of GF-2 satellite image. Arab J Geosci 12, 699 (2019). https://doi.org/10.1007/s12517-019-4879-x

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