Application of multi-scale segmentation algorithms for high resolution remote sensing image

Tingting Zhou; Lingjia Gu; Ruizhi Ren

doi:10.1117/12.2271514

19 September 2017 Application of multi-scale segmentation algorithms for high resolution remote sensing image

Tingting Zhou, Lingjia Gu, Ruizhi Ren

Proceedings Volume 10396, Applications of Digital Image Processing XL; 103961T (2017) https://doi.org/10.1117/12.2271514
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

In recent decades, with the rapid development of remote sensing technology, high resolution remote sensing images have been widely used in various fields due to their characteristics, such as rich spectral information and complex texture information. As a key step in the feature extraction, multi-scale image segmentation algorithm has been a hotspot currently. The traditional image segmentation is based on pixels, which only takes the spectral information of pixel into account, and ignores the texture, spatial information and contextual relation of the objects in the image. The experimental high resolution remote sensing images are from GF-2 and the features of the experimental data are obvious, the edges are clear. By using the statistical region merging (SRM) algorithm, the fractal net evolution approach (FNEA) algorithm and the unsupervised multi-scale segmentation of color images (UMSC) algorithm, this paper analyzes the segmentation effects of three multi-scale segmentation algorithms on the optimal scale and on the same segmentation scale respectively. The experimental results under the optimal scale and the same segmentation scale show that the SRM algorithm outperforms the UMSC algorithm, and UMSC algorithm outperforms the FENA algorithm in multi-scale segmentation.

Citation Download Citation

Tingting Zhou, Lingjia Gu, and Ruizhi Ren "Application of multi-scale segmentation algorithms for high resolution remote sensing image", Proc. SPIE 10396, Applications of Digital Image Processing XL, 103961T (19 September 2017); https://doi.org/10.1117/12.2271514

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