Deep learning-driven end-to-end segmentation of Martian river valleys

Abstract

Martian river valleys are inextricably linked to Martian researches, including the development of the Martian climate, geological development and shallow water ice distribution. The segmentation of Martian river valleys provides materials for scientific research. Due to the absence of water, however, the features of Martian rivers are not obvious, resulting in inefficient segmentation. In order to realize high-accuracy segmentation, we propose an end-to-end segmentation method of Martian river valleys based on deep learning. We put forward the MDR (multi-scale double residual) convolution module and the TA (triple attention) module to improve Unet, and thus, MDR-Unet-TA. In this network, we replace two 3 × 3 convolution layers in Unet with an MDR convolution module, which uses multi-scale convolution to extract features of multiple sizes, and uses the residual connection to avoid gradient disappearance. In addition, we introduce a TA module into the skip connection, which reduces the feature map difference between the encoder and the decoder, and obtains detailed information during decoding. Experimental results demonstrate that compared with current semantic segmentation networks, MDR-Unet-TA obtains higher accuracy, F1 and IOU scores of 98.78%, 95.77% and 95.12% on the simple test set and 97.50%, 95.12% and 93.50% on the complex test set, respectively. Compared with MultiRes Unet, the improvement by MDR-Unet-Tri1 in accuracy, F1, and IOU is 0.92%, 2.40% and 2.70% respectively on the simple test set, and 1.98%, 1.84% and 3.58% respectively on the complex test set. MDR-Unet-TA improves the segmentation performance significantly compared with the original network, and realizes the end-to-end segmentation of Martian river valleys.