Developing computationally efficient processing techniques for massive volumes of hyperspectral data is critical for space-based Earth science and planetary exploration (see for example, (Plaza & Chang, 2008), (Henderson & Lewis, 1998) and the references therein). With the availability of remotely sensed data from different sensors of various platforms with a wide range of spatiotemporal, radio...

Remote sensing of coastal and inland waters requires sensors to have a high spatial resolution to cover the spatial variation of biogeochemical properties in fine scales. High spatial-resolution sensors, however, are usually equipped with spectral bands that are wide in bandwidth (50 nm or wider). In this study, based on numerical simulations of hyperspectral remote-sensing reflectance of optic...

Using hyperspectral measurements made in the field, we show that the effective sea-surface reflectance ρ (defined as the ratio of the surface-reflected radiance at the specular direction corresponding to the downwelling sky radiance from one direction) varies not only for different measurement scans, but also can differ by a factor of 8 between 400 nm and 800 nm for the same scan. This means th...

During the last several years, a number of airborne and satellite hyperspectral sensors have been developed or improved for remote sensing applications (Green, 1988-2000; Vane et al., 1993; Kruse & Boardman, 1999). Imaging spectrometry allows the detection of materials, objects and regions in a particular scene with a high degree of accuracy. Hyperspectral data typically consist of hundreds of ...

2 Acknowledgments 3 Table of contents 4 Chapter 1 Introduction 6 1.1 Thesis objectives, motivations and innovation 7 1.2 Materials and methods 15 1.2.1 The Sierra Nevada, U.S.A (study site 1) 16 1.2.2 The Alps, Bozen, Italy (study site 2) 16 1.2.3 Gola Rainforest National Park, Sierra Leone (study site 3) 17 1.3 Thesis outline 18 1.4 References 19 Chapter 2 – Remote sensing of forested landscap...

Compressed sensing is suitable for remote hyperspectral imaging, as it can simplify the architecture of the onboard sensors. To reconstruct hyperspectral image from pushbroom compressive imaging, we present iterative prediction reconstruction architecture based on total variation in this paper. As the conventional total variation prior is not effective at capturing the correlation within spatia...

Aircraft and satellite Remote Sensing [RS] platforms provide spatial and temporal coverage of oceanic water conditions that are unobtainable by any other cost effective means. The hope of Hyperspectral RS [HRS] data is that it will provide the necessary data stream to simultaneously describe the atmospheric and water column optical properties. The goal of these hyperspectral programs is to deve...

This paper investigates the extent to which the accuracy and speed of classifying Hyperspectral remote sensing images are affected by the application of varying degrees of dimensionality reduction. Three methods have been used for dimensionality reduction: PCA, ICA and random band selection; SVM has been used for classification. The results have been evaluated on both natural and man-made scena...

In this work, we present an algorithm to overcome the computational complexity of hyperspectral (HS) image data to detect multiple targets/endmembers accurately and efficiently by reducing time and complexity. In order to overcome the computational complexity standard deviation and chi square distance metric methods are considered. The number of endmembers is estimated by unbiased iterative cor...