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Sadaf Farkhani


Sadaf Farkhani


Project title: Deep learning spatio-temporal image segmentation in precision farming

Project description:
Precision agriculture involves integration of new technologies such as satellite-based images to reduce soil and crop nursing. There are some important indices in agriculture, which are helpful in field caring, such as biomass, leaf area index, protein index, etc. To predict each of these indices, satellite images, multi-spectral images and radar need to be segmented separately. Then, the information of the multi-sensors is fused consequent to a map of the land areas. Deep learning algorithms show promising results in the classification and segmentation domain, and many papers have been working on improving network architectures. However, segmenting radar and satellite data demands a pixel-wise label as well. Hence, RGB ground-based high resolution images taken sparsely are going to be used in this project.

Through this estimation, three challenges will be met. Firstly, sparse ground-based images need to be globalised as the resolution of satellite-based images is much higher than plants' dimension. Secondly, multi-spectral images are going to be segmented based on labels generated in the first step. Finally, segmented results of radar and multi-spectral images will be fused together.

Supervisor: Prof. (Docent) Henrik Karstoft

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