The Earth Observing One (EO-1) satellite has completed 16 years of Earth observations in early 2017. What started as a technology mission to test various new advancements turned into a science and application mission that extended many years beyond the satellite’s planned life expectancy. EO-1’s primary instruments are spectral imagers: Hyperion, the only civilian full spectrum spectrometer (43...

Hyperspectral imaging sensors suffer from spectral and spatial misregistrations due to optical-system aberrations and misalignments. These artifacts distort spectral signatures that are specific to target objects and thus reduce classification accuracy. The main objective of this work is to detect and correct spectral and spatial misregistrations of hyperspectral images. The Hyperion visible ne...

This paper presents the research results on the integration approach using a biologically inspired algorithm (LEGION) and a geometrically-inspired method (GAC) for target extraction from multiple-source remote-sensing imageries, specifically EO-1 Hyperion hyperspectral (30-meter resolution), and IKONOS multispectral (4-meter resolution) images. An automatic road-extraction algorithm based on LE...

This paper presents an evaluation, to which degree geological and geomorphological information can be obtained from modern remote sensing systems like the multispectral ASTER or the hyperspectral Hyperion sensor for a hyperarid desert region like the Dakhla Oasis (Western Desert, Egypt). To account for the enhanced information content these sensors provide, hyperspectral analysis methods, incor...

In order to determine mineralogy of rock and soil samples, reflectance and emittance spectroscopy in the near-infra red and short-wave infra-red is used extensively and found to be inexpensive. Hyper spectral remote sensing satellite data are found to be prospective to deliver in depth physico-chemistry like mineralogy, chemistry, morphology of the earth’s surface. Therefore hyper spectral data...

Data from the Earth Observing-1 Hyperion instrument were used. Apart from atmospheric influences or topographic effects, the data represent a good choice in order to show different steps of the preprocessing process targeting sensor-internal sources of errors. These include diffuse sensor noise, striping, smile-effect, keystone effect, and spatial misalignments between the detector arrays. For ...

Leaf area index (LAI) is an important bio-physical character of vegetation and can be effectively achieved through remote sensing technology. However the LAI inversion from low resolution data induces a scaling bias due to the heterogeneous of the surface and model non-linearity, which may cause the scale effect on the LAI estimate. In this work, the Yingke oasis of Heihe River is selected as t...

Remote sensing has played a crucial role in mapping and understanding of the spatial pattern of mangrove forests and changes in its areal extent caused by natural disasters and anthropogenic forces. So far traditional pixel-based classification of multispectral imagery has been widely applied for broad mapping of mangrove covers. But the recent and more advanced hyperspectral data taken from se...

The goal of this research was to investigate the potential of hyperspectral Hyperion (EO-1) data to derive fractional cover of rangeland components using constrained linear spectral mixture analysis. Hyperion image data were acquired over the Antelope Creek Ranch located in southern Alberta, Canada in July 2005. These image data were first corrected for the sensor artifacts such as spatial mis-...

Field studies among diverse biomes demonstrate that mass-based nitrogen concentration at leaf and canopy scales is strongly related to carbon uptake and cycling. Combined field and airborne imaging spectrometry studies demonstrate the capacity for accurate empirical estimation of forest canopy N concentration and other biochemical constituents at scales from forest stands to small landscapes. I...