Multi-sensor and Multi-scale data fusion for land cover mapping through Convolutional Neural Networks (CNN).

This repository supports a paper we have submitted to IEEE JSTARS. The study assesses the fusion of Sentinel-1 (S1) and Sentinel-2 (S2) satellite image time series in addition to a Very High Spatial Resolution (VHSR) SPOT image for land cover mapping via a 3 branch CNN architecture. The study was carried out on Reunion island.

The code relies on Pyhton 3.7.6. The CNN models were implemented with Tensorflow 2.

• fusion of S1, S2 and SPOT

python main.py s1_path s2_path ms_path pan_path gt_path

• See help for descriptions python main.py -h/--help

• available cases: S1 and S2, S2 and SPOT, S1, S2 and SPOT

• example for S1 and S2 python main.py s1_path s2_path ms_path pan_path gt_path -s s1 s2

• example for S2 and SPOT python main.py s1_path s2_path ms_path pan_path gt_path -s s2 spot

• See help for all available options

• batch size: -bs default value 256

• learning rate: -lrdefault value 0.0001

• number of epochs: -ep default value 1000