Abstract
On-orbit semantic segmentation can produce the target image tile or image description to reduce the pressure on transmission resources of satellites. In this paper, we propose a fully convolutional network for on-orbit semantic segmentation, namely light-weight edge enhanced network (LEN). For the model to be pruned, we present a new model pruning strategy based on unsupervised clustering. The method is performed according to the \(l_1\)-norm of each filter in the convolutional layer. And it effectively guides the pruning of filters and corresponding feature maps in a short time. In addition, the LEN uses a trainable edge enhanced module called enhanced domain transform to further optimize segmentation performance. The module fully exploits multi-level information of the object to generate the edge map and performs edge-preserving filtering on the coarse segmentation. Experimental results suggest that the models produce competitive results while containing only 1.53 M and 1.66 M parameters respectively on two public datasets: Inria Aerial Image Labeling Dataset and Massachusetts Buildings Dataset.
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Hu, J., Li, L., Lin, Y., Wu, F., Zhao, J. (2019). Light-Weight Edge Enhanced Network for On-orbit Semantic Segmentation. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning. ICANN 2019. Lecture Notes in Computer Science(), vol 11728. Springer, Cham. https://doi.org/10.1007/978-3-030-30484-3_27
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