An experimental comparison of multi-resolution segmentation, SLIC and K-means clustering for object-based classification of VHR imagery-Reference-Cited by-同舟云学术

An experimental comparison of multi-resolution segmentation, SLIC and K-means clustering for object-based classification of VHR imagery

Published:2018-08-03 Issue:18 Volume:39 Page:6020-6036
ISSN:0143-1161
Container-title:International Journal of Remote Sensing
language:en
Short-container-title:International Journal of Remote Sensing

Author:

Kavzoglu T.¹^ORCID,Tonbul H.¹

Affiliation:

1. Department of Geomatics Engineering, Gebze Technical University, Gebze, Kocaeli, Turkey

Publisher

Informa UK Limited

Subject

General Earth and Planetary Sciences

Link

https://www.tandfonline.com/doi/pdf/10.1080/01431161.2018.1506592

Reference46 articles.

1. Achanta, R., A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, 2010. “SLIC Superpixels.” EPFL Technical Report 149300, June. http://www.kev-smith.com/papers/SLIC_Superpixels.pdf.

2. SLIC Superpixels Compared to State-of-the-Art Superpixel Methods

3. Quantitative evaluation of variations in rule-based classifications of land cover in urban neighbourhoods using WorldView-2 imagery

4. Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information

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