The Technion – Israel Institute of Technology was founded in 1912 in Haifa and is the oldest university in Israel and the Middle East. The university, where classes started in the winter of 1924/25, offers degrees in science and engineering and related fields such as architecture, medicine, industrial management, and education. Technion is noted as a global pioneer in multidisciplinary research into fields including energy, nanotechnology, and life science. It has 18 academic departments and 52 research centers, and over 13,000 students.
The Multidimensional Analysis in Remote Sensing (MARS) laboratory from the Department of Mapping and Geoinformation Engineering at the Technion is a partner in the InShaPe project. The research activity within the MARS laboratory focuses on developing algorithms and methodologies for measuring, calibrating, and automatically analyzing spectral data for remote sensing on various scales.
The MARS lab has experience with spectral unmixing for extracting sub-pixel information, image classification, measuring and correcting the Bidirectional Reflectance Distribution Function (BRDF), image and sensor calibration, and data fusion. In addition, the laboratory is equipped with various instruments for multi and hyperspectral imaging. In the InShaPe project, MARS monitors the temperature of the melting pool in laser-based additive manufacturing. Such monitoring is essential to guarantee accurate beam shaping and high-quality manufacturing product. Thus, it requires a fast and stable sensing platform. A multispectral camera provided by the InShape partner, SILIOS technologies, will be used for this purpose. The camera records the reflected light from the fusion bed in eight spectral bands that allow for estimating the temperature through multiband pyrometry. Since the raw data units in such cases are arbitrary and not necessarily physical, methodologies for calibrating the sensing sensor will be developed. Besides, the MARS team will examine various methods for spectral image processing to improve the temperature estimation accuracy, for example, image classification for locally-adaptive estimation, spectral unmixing, and data fusion for spatial resolution enhancement.