Evaluation of Dirichlet Process Gaussian Mixtures for Segmentation on Noisy Hyperspectral Images
This addresses the need for automated and robust segmentation in remote sensing, though it is incremental as it builds on existing mixture models.
The paper tackles the problem of hyperspectral image segmentation by proposing a Dirichlet Process Gaussian Mixture Model that self-regulates scale and cluster number parameters, bypassing manual tuning and achieving similar results on noisy datasets without pre-processing.
Image segmentation is a fundamental step for the interpretation of Remote Sensing Images. Clustering or segmentation methods usually precede the classification task and are used as support tools for manual labeling. The most common algorithms, such as k-means, mean-shift, and MRS, require an extra manual step to find the scale parameter. The segmentation results are severely affected if the parameters are not correctly tuned and diverge from the optimal values. Additionally, the search for the optimal scale is a costly task, as it requires a comprehensive hyper-parameter search. This paper proposes and evaluates a method for segmentation of Hyperspectral Images using the Dirichlet Process Gaussian Mixture Model. Our model can self-regulate the parameters until it finds the optimal values of scale and the number of clusters in a given dataset. The results demonstrate the potential of our method to find objects in a Hyperspectral Image while bypassing the burden of manual search of the optimal parameters. In addition, our model also produces similar results on noisy datasets, while previous research usually required a pre-processing task for noise reduction and spectral smoothing.