New Advances on Sub-pixel Processing: Unmixing and Mapping Methods

Dear Colleagues,

Sub-pixel processing takes into account the possibility of a pixel to belong to different classes in an image segmentation context. This is especially relevant in remote sensing, where different macroscopic or microscopic components could appear to contribute to every pixel. Thus sub-pixel segmentation can increase the resolution of the original images, which is limited by the sensorial systems and several measurement effects. Two main problems are identified in sub-pixel processing: unmixing and mapping. Unmixing refers to the separation of the components contributing to the pixel information (typically a pixel signature obtained from hyperspectral images) to build abundance maps describing the proportion of every component in a given pixel. Mapping is the distribution inside a pixel of its labelled sub-pixels, consistently with the abundance maps.

A good variety of options have been proposed so far, but there is still room for new advances in this challenging problem. Thus nonlinear decomposition techniques like Independent Component Analysis, Bounded Component Analysis or Nonnegative Matrix Factorization, among others, are candidates to improve conventional unmixing methods based on linear models like Principal Component Analysis. On the other hand, sub-pixel mapping is an ill-conditioned problem as far as many possible sub-pixel distributions are compatible with the estimated abundance maps. Advanced solutions from machine learning and pattern analysis and recognition have been also devised to solve the inherent problems of sub-pixel processing. This is symptomatic of the very diverse approaches adopted and highlights the potential of research in this area.

Thus the aim of this special issue is to contribute with new theoretical and practical insights in this booming topic focused to the remote sensing imagery.

Dr. Addisson Salazar
Prof. Luis Vergara
Dr. Gonzalo Safont
Guest Editors

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