Image processing on-board (HR & VHR & Hyperspectral)
This workflow will be demonstrated on a Deep Learning (DL) image processing pipeline devoted to feature extraction in Earth Observation images on-board of small satellites.
This pipeline will be as generic as possible to be able to update, in flight, the performed recognition tasks (clouds, floods, planes, ships or more generic objects identification) with new models required by new applications or users (replace ships detection by oil spills detection, or even fires).
The main challenge of this activity is to define the most suitable combination of methods for DL networks simplification (pruning, compression, …) allowing to execute efficient but complex networks in on-board hardware resources (spatialized or not, including FPGA). These methods are generic enough to be applied to networks or aggregate of networks with different types of architectures. For example:
EfficientNetarchitecture optimizing both accuracy and efficiency
Rethinking Model Scaling
Mobile NetMobile Inverted Bottleneck block
Inverted Residuals and Linear Bottlenecks
SE-ResNextsqueeze and excitation networks