1. Scanner Neural Network for On-Board Segmentation of Satellite Images
- Author
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Lafarge, Florent, Bahl, Gaétan, Geometric Modeling of 3D Environments (TITANE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), and IRT Saint Exupéry - Institut de Recherche Technologique
- Subjects
Satellite Imagery ,On-board processing ,Cloud Segmentation ,ConvLSTM ,ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,Recurrent Convolutional Network ,Image Segmentation - Abstract
International audience; 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.
- Published
- 2022