Water and ice clouds and other aerosols will significantly impact the performance of air and space based hyperspectral remote sensing systems. The effects are seen both in terms of optically thin layers intervening in the sensor line-of-sight and in the impact of cloud shadowed illumination on observed materials. While contamination is expected more often than not, many experiments and algorithms either explicitly or implicitly are biased toward ideal conditions. In this paper we discuss a range of findings regarding the expectation of cloud and aerosol contamination, the automated recognition of that contamination, approaches to minimizing the deleterious effects on remotely sensed signals and the expected impact on material identification performance. Because of a relative paucity of data, simulation plays an important role in these studies; the paper includes a review of current capabilities to replicate cloud impacts through modeling.
Confronting clouds: detection, remediation and simulation approaches for hyperspectral remote sensing systems
2000-01-01
779120 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
British Library Conference Proceedings | 2000
|Hyperspectral remote sensing geological survey instrument
Europäisches Patentamt | 2022
|Hyperspectral Remote Sensing Technology (HRST) Program
British Library Conference Proceedings | 1998
|Hyperspectral Remote Sensing Technology (HRST) program
AIAA | 1997
|