Foreword to the Special Issue on Hyperspectral Image and Signal Processing
This special issue presents advances in signal and image processing related to hyperspectral remote sensing (or imaging spectroscopy). We present the seventy-eight papers selected, which distill the state of the art and the advances in hyperspectral image analysis and its applications. This special issue of JSTARS follows the 6 th workshop on hyperspectral image and signal processing (WHISPERS) held in Lausanne (Switzerland) in June 2014. It contains both extended versions of contributions presented in WHISPERS and regular papers submitted to JSTARS dealing with contemporary aspects of hypersepctral image and signal processing. Main topics show continuity. At a first glimpse, the papers contained in this special issue maintain the methodological flavour of both WHISPERS and the previous special issues [1]–[3]. The main topics remain classification, unmixing, spectral-spatial methods and sparse methods, as one can see in the word cloud extracted form the titles of the papers published in this issue (Fig. 1). This is confirmed by the rough classification of the main topics 1 of the papers, which is reported in the top panel of Fig. 2: classification and unmixing are still the most present ones and, together with the connecting themes of data fusion and feature selection/extraction, cover almost 3/4 of the keywords. However, the rising themes of high performance computing, regression and sparse processing seem to make their way into the favourite topics represented and will surely increase their presence in the future. Hence, the hypersepctral image/signal processing community seems to stick to its favourite themes, while enlarging its vision thanks to the advances in computing power [4] and availability of data [5]. New applications are being tackled, but is it fast enough? From the application summary in the bottom panel of Fig. 2, one can observe that roughly 50% of the papers in the issue validate the proposed methodology on benchmark datasets (e.g. 1 Each paper was assigned to a number of subtopics that matched with its content.