A scalable approach for the efficient segmentation of hyperspectral images

Abstract

The number of applications of hyperspectral imaging (HSI) is steadily increasing, as technology evolves and cameras become more affordable. However, the volume of data in a hyperspectral image is large (order of Gigabytes) and standard off-the-shelf algorithms for multi-channel image analysis cannot be readily applied, due to the prohibitive computational time and large memory requirements. Therefore, new scalable approaches are required to perform hyperspectral image analysis. In this article we address an efficient methodology for conducting Unsupervised Image Segmentation – one of the basic and most fundamental image analysis operations. In the methodology proposed, unsupervised segmentation is conducted after transforming the spectral and spatial dimensions of the raw hyperspectral image into a more compact representation using multivariate and multiresolution techniques. The clusters identified in the compact image representation are then used to train a discriminative classifier. The classifier is then adapted and transferred for application to the raw image, where it will efficiently label all the original pixels. With the proposed methodology, the computational expensive operations (unsupervised clustering and classifier learning) are minimized, whereas the efficient implementation of the classifier guarantees the analysis at the native resolution. The effectiveness of the proposed methodology was tested on a real case study considering an industrial hyperspectral image capturing the reflectance spectrum for several objects made of different unknown materials. A significant reduction in the computational cost was achieved without compromising the quality of the unsupervised segmentation, demonstrating the potential of the proposed approach.