Abstract
The development of the hyperspectral remote sensor technology allows the acquisition of images with a very detailed spectral information for each pixel. Because of this, hyperspectral images (HSI) potentially possess larger capabilities in solving many scientific and practical problems in agriculture, biomedical, ecological, geological, hydrological studies. However, their analysis requires developing specialized and fast algorithms for data processing, due the high dimensionality of the data. In this work, we propose a new semi-supervised method for multilabel segmentation of HSI that combines a suitable linear discriminant analysis, a similarity index to compare different spectra, and a random walk based model with a direct label assignment. The user-marked regions are used for the projection of the original high-dimensional feature space to a lower dimensional space, such that the class separation is maximized. This allows to retain in an automatic way the most informative features, lightening the successive computational burden. The part of the random walk is related to a combinatorial Dirichlet problem involving a weighted graph, where the nodes are the projected pixel of the original HSI, and the positive weights depend on the distances between these nodes. We then assign to each pixel of the original image a probability quantifying the likelihood that the pixel (node) belongs to some subregion. The computation of the spectral distance involves both the coordinates in a features space of a pixel and of its neighbors. The final segmentation process is therefore reduced to a suitable optimization problem coupling the probabilities from the random walker computation, and the similarity with respect the initially labeled pixels. We discuss the properties of the new method with experimental results carried on benchmark images.
Highlights
Hyperspectral imaging systems have gained a great amount of attention from researchers in the past few years
In order to label the last vertices we develop a random walker approach which improves the quality of the image segmentation and involves only the minimization of a quadratic function
The very first step is to acquire the user-marked region in the original image: this labels are the building blocks of the entire procedure, since they are employed to get the optimal projection of the hyperspectral image and the feature image; they are used to compute the centroids from the feature image; they represent the seeds for the random walker method
Summary
Hyperspectral imaging systems have gained a great amount of attention from researchers in the past few years. The high dimensionality of the hyperspectral data, for example, the spectral reflectance values of hyperspectral images collected by airborne or space-borne imaging spectrometers, is up to hundreds of dimensions Factors such as sensors, atmospheric conditions, surrounding environment, and composition and distribution of ground features affect the spatial variability of spectral information. The idea of applying subspace projection approach relies on the assumption that the samples within each class can approximately lie in a lower-dimensional subspace Due to their successful application in several problems of pattern recognition neural networks have attracted many researchers in the field of the classification of hyperspectral images [28,29,30,31]. The new similarity index includes the contextual spatial information provided in the HSI data considering features of adjacent pixels together. Following the findings of the case study, the conclusions are presented in the last Section 4 with a discussion about the properties and the possible developments of the approach
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