A Dual Network for Super-Resolution and Semantic Segmentation of Sentinel-2 Imagery

Remote Sensing

Javier Marcello¹  Luis Salgueiro²  Verónica Vilaplana²  Saüc Abadal² 

[1] Instituto de Oceanografía y Cambio Global (IOCAG), Unidad Asociada ULPGC-CSIC, 35017 Las Palmas de Gran Canaria, Spain;Signal Theory and Communications Department, Universitat Politècnica de Catalunya (UPC), 08034 Barcelona, Spain;

关键词: super-resolution;    semantic segmentation;    deep learning;    convolutional neural network;    Sentinel-2;   

DOI  :  10.3390/rs13224547

来源: DOAJ

【 摘 要 】

There is a growing interest in the development of automated data processing workflows that provide reliable, high spatial resolution land cover maps. However, high-resolution remote sensing images are not always affordable. Taking into account the free availability of Sentinel-2 satellite data, in this work we propose a deep learning model to generate high-resolution segmentation maps from low-resolution inputs in a multi-task approach. Our proposal is a dual-network model with two branches: the Single Image Super-Resolution branch, that reconstructs a high-resolution version of the input image, and the Semantic Segmentation Super-Resolution branch, that predicts a high-resolution segmentation map with a scaling factor of 2. We performed several experiments to find the best architecture, training and testing on a subset of the S2GLC 2017 dataset. We based our model on the DeepLabV3+ architecture, enhancing the model and achieving an improvement of 5% on IoU and almost 10% on the recall score. Furthermore, our qualitative results demonstrate the effectiveness and usefulness of the proposed approach.

【 授权许可】

Unknown