期刊
REMOTE SENSING
卷 13, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/rs13224547
关键词
super-resolution; semantic segmentation; deep learning; convolutional neural network; Sentinel-2
类别
资金
- Spanish Research Agency (AEI) [PID2020-117142GB-I00, MCIN/AEI/10.13039/501100011033]
This study presents a method that uses a deep learning model to generate high-resolution segmentation maps from low-resolution images. By training and testing on Sentinel-2 satellite data, improvements were made to the DeepLabV3+ architecture, resulting in promising results.
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.
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