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Remote Sensing Image Segmentation Based on a Novel Gaussian Mixture Model and SURF Algorithm

Remote Sensing Image Segmentation Based on a Novel Gaussian Mixture Model and SURF Algorithm

Shoulin Yin, Liguo Wang, Qunming Wang, Jinghui Yang, Man Jiang
Copyright: © 2023 |Volume: 14 |Issue: 2 |Pages: 15
ISSN: 1947-9263|EISSN: 1947-9271|EISBN13: 9798369303849|DOI: 10.4018/IJSIR.322301
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MLA

Yin, Shoulin, et al. "Remote Sensing Image Segmentation Based on a Novel Gaussian Mixture Model and SURF Algorithm." IJSIR vol.14, no.2 2023: pp.1-15. http://doi.org/10.4018/IJSIR.322301

APA

Yin, S., Wang, L., Wang, Q., Yang, J., & Jiang, M. (2023). Remote Sensing Image Segmentation Based on a Novel Gaussian Mixture Model and SURF Algorithm. International Journal of Swarm Intelligence Research (IJSIR), 14(2), 1-15. http://doi.org/10.4018/IJSIR.322301

Chicago

Yin, Shoulin, et al. "Remote Sensing Image Segmentation Based on a Novel Gaussian Mixture Model and SURF Algorithm," International Journal of Swarm Intelligence Research (IJSIR) 14, no.2: 1-15. http://doi.org/10.4018/IJSIR.322301

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Abstract

This paper proposes a novel remote sensing image segmentation method based on Gaussian mixture model and SURF algorithm. Firstly, Gaussian mixture model is used for remote sensing image segmentation. Then the surf matching algorithm is adopted for eliminating misidentified areas. The determinant of Hession matrix (DoH) is used to detect key points in the image. The non-maximum suppression method and interpolation operation are utilized to search and locate the extreme points. The maximum likelihood method is used to estimate model parameters. Some remote sensing images in THE DOTA data set are selected for experimental verification, and the results show that the new algorithm has obvious improvement in segmentation effect and efficiency. In the background complex image segmentation, the improved algorithm has more obvious advantages compared than state-of-the-art segmentation methods.