Hyperspectral Image Clustering with Spatially-Regularized Ultrametrics

We propose a method for the unsupervised clustering of hyperspectral images based on spatially regularized spectral clustering with ultrametric path distances. The proposed method efficiently combines data density and geometry to distinguish between material classes in the data, without the need for training labels. The proposed method is efficient, with quasilinear scaling in the number of data points, and enjoys robust theoretical performance guarantees. Extensive experiments on synthetic and real HSI data demonstrate its strong performance compared to benchmark and state-of-the-art methods. In particular, the proposed method achieves not only excellent labeling accuracy, but also efficiently estimates the number of clusters.Comment: 5 pages, 2 columns, 9 figure

Hyperspectral images segmentation: a proposal

Hyper-Spectral Imaging (HIS) also known as chemical or spectroscopic imaging is an emerging technique that combines imaging and spectroscopy to capture both spectral and spatial information from an object. Hyperspectral images are made up of contiguous wavebands in a given spectral band. These images provide information on the chemical make-up profile of objects, thus allowing the differentiation of objects of the same colour but which possess make-up profile. Yet, whatever the application field, most of the methods devoted to HIS processing conduct data analysis without taking into account spatial information.Pixels are processed individually, as an array of spectral data without any spatial structure. Standard classification approaches are thus widely used (k-means, fuzzy-c-means hierarchical classification...). Linear modelling methods such as Partial Least Square analysis (PLS) or non linear approaches like support vector machine (SVM) are also used at different scales (remote sensing or laboratory applications). However, with the development of high resolution sensors, coupled exploitation of spectral and spatial information to process complex images, would appear to be a very relevant approach. However, few methods are proposed in the litterature. The most recent approaches can be broadly classified in two main categories. The first ones are related to a direct extension of individual pixel classification methods using just the spectral dimension (k-means, fuzzy-c-means or FCM, Support Vector Machine or SVM). Spatial dimension is integrated as an additionnal classification parameter (Markov fields with local homogeneity constrainst [5], Support Vector Machine or SVM with spectral and spatial kernels combination [2], geometrically guided fuzzy C-means [3]...). The second ones combine the two fields related to each dimension (spectral and spatial), namely chemometric and image analysis. Various strategies have been attempted. The first one is to rely on chemometrics methods (Principal Component Analysis or PCA, Independant Component Analysis or ICA, Curvilinear Component Analysis...) to reduce the spectral dimension and then to apply standard images processing technics on the resulting score images i.e. data projection on a subspace. Another approach is to extend the definition of basic image processing operators to this new dimensionality (morphological operators for example [1, 4]). However, the approaches mentioned above tend to favour only one description either directly or indirectly (spectral or spatial). The purpose of this paper is to propose a hyperspectral processing approach that strikes a better balance in the treatment of both kinds of information....Cet article présente une stratégie de segmentation d’images hyperspectrales liant de façon symétrique et conjointe les aspects spectraux et spatiaux. Pour cela, nous proposons de construire des variables latentes permettant de définir un sous-espace représentant au mieux la topologie de l’image. Dans cet article, nous limiterons cette notion de topologie à la seule appartenance aux régions. Pour ce faire, nous utilisons d’une part les notions de l’analyse discriminante (variance intra, inter) et les propriétés des algorithmes de segmentation en région liées à celles-ci. Le principe générique théorique est exposé puis décliné sous la forme d’un exemple d’implémentation optimisé utilisant un algorithme de segmentation en région type split and merge. Les résultats obtenus sur une image de synthèse puis réelle sont exposés et commentés

Reducing Dimensionality of Hyperspectral Data with Diffusion Maps and Clustering with K-means and Fuzzy ART

It is very difficult to analyse large amounts of hyperspectral data. Here we present a method based on reducing the dimensionality of the data and clustering the result in moving toward classification of the data. Dimensionality reduction is done with diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original dataset in order to obtain an efficient representation of data geometric descriptions. Clustering is done using k-means and a neural network clustering theory, Fuzzy ART (FA). The process is done on a subset of core data from AngloGold Ashanti, and compared to results obtained by AngloGold Ashanti\u27s proprietary method. Experimental results show that the proposed methods are promising in addressing the complicated hyperspectral data and identifying the minerals in core samples

Implementation strategies for hyperspectral unmixing using Bayesian source separation

Bayesian Positive Source Separation (BPSS) is a useful unsupervised approach for hyperspectral data unmixing, where numerical non-negativity of spectra and abundances has to be ensured, such in remote sensing. Moreover, it is sensible to impose a sum-to-one (full additivity) constraint to the estimated source abundances in each pixel. Even though non-negativity and full additivity are two necessary properties to get physically interpretable results, the use of BPSS algorithms has been so far limited by high computation time and large memory requirements due to the Markov chain Monte Carlo calculations. An implementation strategy which allows one to apply these algorithms on a full hyperspectral image, as typical in Earth and Planetary Science, is introduced. Effects of pixel selection, the impact of such sampling on the relevance of the estimated component spectra and abundance maps, as well as on the computation times, are discussed. For that purpose, two different dataset have been used: a synthetic one and a real hyperspectral image from Mars.Comment: 10 pages, 6 figures, submitted to IEEE Transactions on Geoscience and Remote Sensing in the special issue on Hyperspectral Image and Signal Processing (WHISPERS