Scanner Neural Network for On-board Segmentation of Satellite Images

Florent Lafarge (1) , Gaétan Bahl (1, 2)

1 TITANE - Geometric Modeling of 3D Environments
2 IRT Saint Exupéry - Institut de Recherche Technologique

Traditional Convolutional Neural Networks (CNN) for semantic segmentation of images use 2D convolution operations. While the spatial inductive bias of 2D convolutions allow CNNs to build hierarchical feature representations, they require that the whole feature maps are kept in memory until the end of the inference. This is not ideal for memory and latency-critical applications such as real-time on-board satellite image segmentation. In this paper, we propose a new neural network architecture for semantic segmentation, "Scan-nerNet", based on a Recurrent 1D Convolutional architecture. Our network performs a segmentation of the input image lineby-line, and thus reduces the memory footprint and output latency. These characteristics make it ideal for on-the-fly segmentation of images on-board satellites equipped with push broom sensors such as Landsat 8, or satellites with limited compute capabilities, such as Cubesats. We perform cloud segmentation experiments on embedded hardware and show that our method offers a good compromise between accuracy, memory usage and latency.

Mots clés: Image Segmentation; Satellite Imagery; On-board processing; Recurrent Convolutional Network; ConvLSTM; Cloud Segmentation

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Florent Lafarge, Gaétan Bahl. Scanner Neural Network for On-board Segmentation of Satellite Images. IGARSS 2022 – IEEE International Geoscience and Remote Sensing Symposium, Jul 2022, Kuala Lumpur, Malaysia. ⟨hal-03664644⟩