This paper covers 60 years of terrestrial imaging spectroscopy or hyperspectral remote sensing – 30 years each devoted to past and future developments. For this purpose, a brief history of imaging spectroscopy is given, covering civilian airborne sensor and spaceborne mission developments and associated data processing capabilities. Moreover, the paper reviews the existing and planned space sys...

Citation: Hedley JD, Russell BJ, Randolph K, Pérez-Castro MÁ, Vásquez-Elizondo RM, Enríquez S and Dierssen HM (2017) Remote Sensing of Seagrass Leaf Area Index and Species: The Capability of a Model Inversion Method Assessed by Sensitivity Analysis and Hyperspectral Data of Florida Bay. Front. Mar. Sci. 4:362. doi: 10.3389/fmars.2017.00362 Remote Sensing of Seagrass Leaf Area Index and Species:...

This paper studies the effect of synthetic feature vectors on the classification performance of hyperspectral remote sensing images. As feature vectors, it has been chosen to employ morphological attribute profiles, that have proven themselves in this field. At this early stage of our work, the relatively simple Bootstrapping algorithm has been used for synthetic feature vector generation. Base...

Dimensionality reduction and segmentation have been used as methods to reduce the complexity of the representation of hyperspectral remote sensing images. In this study, a new object-oriented mapping approach is proposed based on nonlinear subspace feature analysis of hyperspectral remote sensing images. Nonlinear local manifold learning approaches for feature extraction were utilized to obtain...

For a data set collected around Baja California with chlorophyll-a concentration ((chl-a)) ranging from 0.16 to 11.3 mg/m, hyperspectral absorption spectra of phytoplankton pigments were independently inverted from hyperspectral remote-sensing reflectance using a newly developed ocean-color algorithm. The derived spectra were then compared with those measured from water samples using the filter...

Constrained Energy Minimization algorithm is used in hyperspectral remote sensing target detection, it only needs the spectrum of interest targets, knowledge of background is unnecessary, so it is well applied in hyperspectral remote sensing target detection. This paper analyzed the reason of better results in small target detections and worse ones in large target detections of CEM algorithm, a...

The term hyperspectral is used to refer to spectra consisting of large number of narrow, contiguously spaced spectral bands. In the field of remote sensing, the term hyperspectral is also used interchangeably with other terms such as spectroscopy, spectrometry, spectroradiometry and rarely ultraspectral. Spectroscopy is a branch of physics concerned with the production, transmission, measuremen...

The technology of hyperspectral remote sensing image improves the capability of collecting the objects such as lakes, rivers, farmlands, buildings, forest and desert in the ground surface. Since the spatial resolution is becoming higher recently, image segmentation of hyperspectral remote sensing is important to the next step of remote sensing image classification and object recognition. In thi...

Hyperspectral remote sensing (HRS) and imaging spectroscopy (IS) are the same technologies that provide detailed spectral information for individual pixels of an image. While HRS refers mostly to remote sensing (from a distance), the emerging IS technology covers wide spatial–spectral domains, frommicroscopic to macroscopic HRS/IS. IS is an innovative development of the charge-coupled device (C...

Remote sensing is being increasingly used in different agricultural applications. Hyperspectral remote sensing in large continuous narrow wavebands provides significant advancement in understanding the subtle changes in biochemical and biophysical attributes of the crop plants and their different physiological processes, which otherwise are indistinct in multispectral remote sensing. This artic...