Cultivated land segmentation in RGB remote sensing images: non-uniform regularization with kernel space and graph cut

Wangsheng Wu

doi:10.1117/12.3033562

13 June 2024 Cultivated land segmentation in RGB remote sensing images: non-uniform regularization with kernel space and graph cut

Wangsheng Wu

Author Affiliations +

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 131800P (2024) https://doi.org/10.1117/12.3033562
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

To improve the application efficiency of RGB remote sensing images in agricultural land resource surveys, a cultivated land segmentation algorithm based on kernel space non-uniform regularization classification and improved graph cut was proposed. Firstly, extracting texture and color features of remote sensing images using Local Binary Pattern algorithm (LBP), Gabor filters, and RGB, HSV color space, respectively. Next, introducing a kernel method to map data from lowdimension to high-dimension, and construct a kernel space-based non-uniform regularization sparse representation model to classify and segment images in pixel level. Finally, an innovative graph cut algorithm is enhanced by incorporating a Gaussian distribution to redefine the penalty term for homogeneous regions and introducing a new color gradient measure to define the penalty term for boundaries. This approach effectively removes scatter and restricts the segmentation boundary. The average classification accuracy and average F1 score of the classifier proposed in this paper are about 2% and 3% higher than those of recent regularized subspace classifiers, respectively. Compared with the Graph cut algorithm, the proposed improved graph cut algorithm has an average mIoU improvement of about 9%. The average accuracy of the whole segmentation algorithm is 95.43%, and the average mIoU is 88.56%. Compared with the comparison algorithm, the proposed algorithm has higher segmentation accuracy, which proves that the proposed algorithm can adapt to the cultivated land segmentation scene of remote sensing images and is effective.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

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Wangsheng Wu "Cultivated land segmentation in RGB remote sensing images: non-uniform regularization with kernel space and graph cut", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 131800P (13 June 2024); https://doi.org/10.1117/12.3033562

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KEYWORDS

Image segmentation

Remote sensing

RGB color model

Image classification

Image processing algorithms and systems

Associative arrays

Statistical analysis

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