Enayat Hosseini Aria
Remote sensing

I am a PhD in remote sensing, graduated (2018) from the Department of Geoscience and Remote sensing, Delft University of Technology, the Netherlands; with an interest in exploring remote sensing data; mathematical models and machine learning algorithms; and developing methods for a deeper understanding of our environment. During my PhD, I have developed and implemented a methodology to extract optimal information from hyperspectral images while reducing the dimensionality of the data; leading to higher accuracy in land cover classification. Later, the proposed algorithm was embedded in an onboard processing chain of an ESA hyperspectral satellite, called HyperScout, which successfully operated in the orbit. I have also collaborated in different projects such as analysing a time series of the high resolution of optical images for crop water requirement in the north of Africa, and the development of a data fusion algorithm using MODIS, Landsat-8, and Sentinel-2 to generate TIR images for soil moisture retrieval.

 

Since Nov. 2020, I joined the Department of Biodiversity, Ecole d’Ingénieurs de PURPAN, Toulouse, as a postdoctoral researcher focusing on the development of operational services for monitoring vines status by exploring the potential of multi/high spectral imagery (drone, airborne and satellite images) and deep learning algorithms.


Important publications:

  1. Hosseini Aria, S. E.; Menenti, M.; Gorte, B.; Homayouni, S., "An unsupervised dimensionality reduction of hyperspectral images using representations of reflectance spectra", , 2020.

  2. N. Jamshidpour, E. Hosseini Aria, A. Safari and S. Homayouni, "Adaptive Self-Learned Active Learning Framework for Hyperspectral Classification," 2019 10th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS), 2019, pp. 1-5, doi: 10.1109/WHISPERS.2019.8921298.

  3. Aria, S. E., M. Menenti, et al. (2017). "Spectral region identification versus individual channel selection in supervised dimensionality reduction of hyperspectral image data." Journal of Applied Remote Sensing 11(4): 046010.

  4. Conticello, S. S.; Esposito, M.; Foglia Manzillo, P.; Van Dijk, C.; Vercruyssen, N.; Baeck, P.-J.; Benhadj, I.; Livens, S.; Delaure, B.; Soukup, M.; Jochemsen, A.; Aas, C.; Gorte, B.; Hosseini Aria, E.; Menenti, M., Hyperspectral imaging for real-time land and vegetation inspection from nanosatellite. In , ESA: MALTA, 2016.

  5. Li, J.; Donselaar, M. E.; Hosseini Aria, S. E.; Koenders, R.; Oyen, A., Landsat imagery-based visualization of the geomorphological development at the terminus of a dryland river system. 2014, , 100-110.

  6. Oyen, A. M.; Koenders, R.; Hosseini Aria, S. E.; Lindenbergh, R. C.; Li, J.; Donselaar, M. E. In Application of synthetic aperture radar methods for morphological analysis of the Salar De Uyuni distal fluvial system, , , 22-27 July 2012; pp 3875-3878.