Yosef Akhtman

Direct Georeferencing of a Pushbroom, Lightweight Hyperspectral System for Mini-UAV Applications

Hyperspectral imagery has proven its potential in many research applications, especially in the field of environmental sciences. Currently, hyperspectral imaging is generally performed by satellite or aircraft platforms, but mini-UAV (Unmanned Aerial Vehicle) platforms (<20 kg) are now emerging. On such platforms, payload restrictions are critical, so sensors must be selected according to stringent specifications. This article presents the integration of a light pushbroom hyperspectral sensor onboard a multirotor UAV, which we have called Hyper-DRELIO (Hyperspectral DRone for Environmental and LIttoral Observations). This article depicts the system design: the UAV platform, the imaging module, the navigation module, and the interfacing between the different elements. Pushbroom sensors offer a better combination of spatial and spectral resolution than full-frame cameras. Nevertheless, data georectification has to be performed line by line, the quality of direct georeferencing being limited by mechanical stability, good timing accuracy, and the resolution and accuracy of the proprioceptive sensors. A georegistration procedure is proposed for geometrical pre-processing of hyperspectral data. The specifications of Hyper-DRELIO surveys are described through two examples of surveys above coastal or inland waters, with different flight altitudes. This system can collect hyperspectral data in VNIR (Visible and Near InfraRed) domain above small study sites (up to about 4 ha) with both high spatial resolution (<10 cm) and high spectral resolution (1.85 nm) and with georectification accuracy on the order of 1 to 2 m.

Leman-Baikal: Remote sensing of lakes using an ultralight plane

The Leman-Baikal project constitutes an international Swiss-Russian collaborative research initiative in the field of physical limnology. The three-year framework involves the development and deployment of a novel multispectral and hyperspectral remote sensing platform optimised for the sensing of land and water surfaces from an ultralight aircraft. In this paper we discuss the developed remote sensing methodology and the initial obtained results.

A new smoothness based strategy for semi-supervised atmospheric correction: Application to the léman-Baïkal campaign

The estimation of reflectance from hyperspectral data, a process called atmospheric correction, is a critical first step in hyperspectral image processing. Most of the current methods need additional measurements of ground reflectances or atmosphere. Without these measurements, unsupervised methods as Quick Atmospheric Correction (QUAC) can be applied but the observed scene has to be composed by several distinct materials. When studies on specific materials are conducted, no such large diversity occurs. To solve the atmospheric correction in that case, we propose a new atmospheric correction method, called Smoothing Technique for Empirical Atmospheric Correction (STEAC), using the smoothness property of the reflectance. This method is benchmarked against QUAC method on images acquired during the Léman-Baïkal campaign. Results shows that this new method outstands QUAC method, both in terms of accuracy and stability with respect to the scene heterogeneity.