Minimax Bridgeness-Based Clustering for Hyperspectral Data

Abstract

Hyperspectral (HS) imaging has been used extensively in remote sensing applications like agriculture, forestry, geology and marine science. HS pixel classification is an important task to help identify different classes of materials within a scene, such as different types of crops on a farm. However, this task is significantly hindered by the fact that HS pixels typically form high-dimensional clusters of arbitrary sizes and shapes in the feature space spanned by all spectral channels. This is even more of a challenge when ground truth data is difficult to obtain and when there is no reliable prior information about these clusters (e.g., number, typical shape, intrinsic dimensionality). In this letter, we present a new graph-based clustering approach for hyperspectral data mining that does not require ground truth data nor parameter tuning. It is based on the minimax distance, a measure of similarity between vertices on a graph. Using the silhouette index, we demonstrate that the minimax distance is more suitable to identify clusters in raw hyperspectral data than two other graph-based similarity measures: mutual proximity and shared nearest neighbours. We then introduce the minimax bridgeness-based clustering approach, and we demonstrate that it can discover clusters of interest in hyperspectral data better than comparable approaches.

Improving K-Nearest Neighbor Approaches for Density-Based Pixel Clustering in Hyperspectral Remote Sensing Images

“…Note that applying MNN naturally associates with each x i a set of K i ≤ K NNs. Several other graph modification methods exist [52,53], but from our experience [54], the MNN graph modification provides better results than the natural nearest neighbor (3N) approach of Zou and Zhu [55].…”

A Semi-Supervised Reduced-Space Method for Hyperspectral Imaging Segmentation

“…This section is devoted to compare the proposed strategy with state-of-the-art methods, namely K-MBC [21], FCM [66], FDPC [67], and GWEEN [68], when the true number of clusters is known. The seeds employed for our method are randomly chosen in the ground truth mask: for each marked region, two small squares with size 7 pixels are selected.…”

“…In principle, the spectral information from the available hundreds of narrow bands collected by hyperspectral sensors can help discriminate among spectrally similar object pixels. Then, the accurate discrimination of different regions in the image is possible and the hyperspectral image classification is one of the most active part of the research in the hyperspectral field [18,[20][21][22]. However, the HSI technology still faces a series of challenges, mainly including the following problems that need to be solved.…”

Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics

“…In particular, only very weak assumptions on the data are required, namely that the underlying clusters exhibit intrinsically low-dimensional structure and are separated by regions of low density. This suggests UPD are well-suited for HSI [13], which while very high-dimensional, are typically such that each class in the data depends (perhaps nonlinearly) on only a small number of latent variables, and in this sense are intrinsically low-dimensional.…”