**1. Introduction**

Over the past years hyperspectral imaging has received considerable interests [1] such as parallel processing [2], real-time processing [3,4]. It deviates from traditional spatial domain-based image processing and multispectral imaging in many di fferent ways. It has attracted many people from di fferent disciplinary areas to explore new ideas and new applications [5]. In recent years, a significant increase in publications in hyperspectral imaging has provided evidence that hyperspectral image processing has broken away from traditional spatial domain analysis-based remote sensing and successfully branched out to stand alone as a potential and promising research area. Most importantly, hyperspectral imaging have also changed many ways in which algorithms are designed and developed. As a consequence, many problems such as subpixels and mixed pixels that are generally encountered in hyperspectral imaging have become major issues for traditional spatial domain-based techniques [6]. Also, the traditional concept of "*seeing-is-believing*" by visual inspection may no longer true in hyperspectral imaging since targets of interest may be completely embedded in a single pixel or partially but not fully occupy a single pixel in which case only spectral properties that can be used to characterize such targets for data analysis. Therefore, this Special Issue "Hyperspectral Imaging and Applications" is devoted to topics which can demonstrate the utility of hyperspectral imaging in data exploitation and to further explore its potential in di fferent applications. This Special Issue has accepted and published 25 papers in various areas, which can be organized into 7 categories with the number of papers published in every category included in its open parenthesis.

1. Data Unmixing (2 papers)


Under every category each paper is briefly summarized by a short description in the following section so that readers can quickly grab its content to find what they are interested in.

#### **2. Overview of Published Papers**

#### **Part I: Data Unmixing (2 papers)**
