Unsupervised Segmentation of Hyperspectral Remote Sensing Images with Superpixels
This is an incremental improvement for remote sensing applications, offering a more automated segmentation approach without predefined parameters.
The paper tackles unsupervised segmentation of hyperspectral remote sensing images by combining mean-shift clustering with superpixels, eliminating the need for class numbers or prior land-cover knowledge, and shows validity through experiments on datasets like Salinas and Pavia with metrics such as normalized mutual information and F1-score.
In this paper, we propose an unsupervised method for hyperspectral remote sensing image segmentation. The method exploits the mean-shift clustering algorithm that takes as input a preliminary hyperspectral superpixels segmentation together with the spectral pixel information. The proposed method does not require the number of segmentation classes as input parameter, and it does not exploit any a-priori knowledge about the type of land-cover or land-use to be segmented (e.g. water, vegetation, building etc.). Experiments on Salinas, SalinasA, Pavia Center and Pavia University datasets are carried out. Performance are measured in terms of normalized mutual information, adjusted Rand index and F1-score. Results demonstrate the validity of the proposed method in comparison with the state of the art.