An unsupervised remote sensing image segmentation method based on hidden Markov model

Xinpeng Man; Yinglei Song

doi:10.1117/12.2682584

1 June 2023 An unsupervised remote sensing image segmentation method based on hidden Markov model

Xinpeng Man, Yinglei Song

Proceedings Volume 12710, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023); 127100B (2023) https://doi.org/10.1117/12.2682584
Event: International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023), 2023, Changsha, China

Abstract

Remote sensing image segmentation is an important problem in the processing of remote sensing images. Existing methods for remote sensing image segmentation include supervised, weakly supervised and unsupervised approaches. Supervised and weakly supervised approaches require certain prior statistical knowledge on different regions in remote sensing images, while unsupervised approaches are able to accomplish the task of segmentation to a certain extent in the absence of such knowledge. The purpose of this paper is to realize an unsupervised image segmentation method that can be applied to remote sensing images. The approach utilizes a hidden Markov model to accurately describe the statistical distributions of the R, G and B components of pixels and the correlations among those of different pixels. The labels in a segmentation result are described by the states in the hidden Markov model and the segmentation with the maximum likelihood is obtained with a dynamic programming approach based on the Viterbi’s algorithm. Experimental results prove the feasibility of the proposed approach for segmentation. A comparison with state-of-the-art segmentation methods show that the proposed approach can lead to segmentation results with improved accuracy. The proposed approach is thus potentially useful for improving the accuracy of remote sensing applications that require segmentations of remote sensing images.

Citation Download Citation

Xinpeng Man and Yinglei Song "An unsupervised remote sensing image segmentation method based on hidden Markov model", Proc. SPIE 12710, International Conference on Remote Sensing, Surveying, and Mapping (RSSM 2023), 127100B (1 June 2023); https://doi.org/10.1117/12.2682584

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KEYWORDS

Image segmentation

Remote sensing

Image processing

Computer programming

Image processing algorithms and systems

RGB color model

Matrices

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