1. Target detection in hyperspectral imagery using forward modeling and in-scene information
- Author
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Ola Friman, Trym Vegard Haavardsholm, Maria Axelsson, and Ingmar Renhorn
- Subjects
010504 meteorology & atmospheric sciences ,Computer science ,business.industry ,0211 other engineering and technologies ,Hyperspectral imaging ,Subspace model ,02 engineering and technology ,01 natural sciences ,Reflectivity ,Atomic and Molecular Physics, and Optics ,Computer Science Applications ,Atmospheric radiative transfer codes ,Radiance ,Computer vision ,Artificial intelligence ,Computers in Earth Sciences ,business ,Engineering (miscellaneous) ,021101 geological & geomatics engineering ,0105 earth and related environmental sciences ,Remote sensing - Abstract
This work addresses the problem of detecting and classifying materials and targets in hyperspectral images based on their reflectance spectrum. Accurate target detection in hyperspectral imagery requires a radiative transfer model that maps between the spectral reflectance domain and the measured radiance domain. Such a model can be employed in two ways for detection – using atmospheric compensation, where the measured hyperspectral radiance image is converted to a reflectance image, and using forward modeling, where the target reflectance spectrum is converted to an at-sensor target radiance spectrum. This work presents a forward modeling detection method that utilizes in-scene information to estimate the parameters in the radiative transfer model. Uncertainty in the radiative transfer model and variability of the target spectra are captured using a constrained subspace model for the target. Target detection using library spectra and target rediscovery are evaluated in hyperspectral images of a complex urban scene.
- Published
- 2016