Presentation + Paper
3 October 2019 Assessment of informative capability of spaced-based hyperspectral system in forest monitoring tasks
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Abstract
The article presents a model for assessing the information capabilities of multi-, hyperspectral satellite systems for Earth remote sensing in solving forest monitoring problems. The information capabilities of satellite system for Earth remote sensing means the ability to complete assigned tasks in time and quality. Assessment of information capabilities is divided into two parts. The first part is an assessment of the operational capabilities of the satellite system, that is, an expected time to complete the task. The second part is an assessment of solving the problems quality. Assessing of the information capabilities of satellite systems for Earth remote sensing allows to determine the appropriateness of including the task of monitoring territories (for example, forest monitoring) in the flight task for the satellite system, to develop measures to improve information capability in solving the tasks. The article also provides informational capability assessment obtained by the proposed method for the hyperspectral satellite system by NPO «Lepton» and Moscow Institute of Physics and Technology in solving the problem of classifying the species composition of deciduous and coniferous forest on a given territory. Such assessment is valid for similar satellite systems for Earth remote sensing when solving similar problems.
Conference Presentation
© (2019) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
S. A. Zotov, Y. V. Dmitriev, and S. Y. Shibanov "Assessment of informative capability of spaced-based hyperspectral system in forest monitoring tasks", Proc. SPIE 11156, Earth Resources and Environmental Remote Sensing/GIS Applications X, 1115603 (3 October 2019); https://doi.org/10.1117/12.2533153
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KEYWORDS
Remote sensing

Satellites

Sensing systems

Image classification

Artificial intelligence

Hyperspectral systems

Classification systems

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