Awesome

Multi-Scale-and-Depth-CNN-for-Pan-sharpening

（1）This matlab code includes the implementation of two deep convolutional networks for fusion of MS and Pan images： MSDCNN: Q. Yuan, Y. Wei, X. Meng, et al, A Multiscale and Multidepth Convolutional Neural Network for Remote Sensing Imagery Pan-Sharpening[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(3):978-989.

DRPNN: Wei Y, Yuan Q, Shen H, et al. Boosting the Accuracy of Multispectral Image Pansharpening by Learning a Deep Residual Network[J]. IEEE Geoscience & Remote Sensing Letters, 2017, 14(10):1795-1799.

（2）Before running the code you need to at least compile MatConvNet on your device. Caffe and MatCaffe are also recommended if you want to train your own model.

*The currently given model only supports fusion of MS image with 4 bands and PAN image with 1 band, such as QuickBird, IKONOS and Pleiades.

（3）If you simply want to use the models trained by the author to fuse your data:

a. Set your matlab path to this folder and add all sub-folders.

b. Put your MS and Pan images in ./testdata and use their filenames to replace 'QB_MS' and 'QB_PAN'in the file 'Demo_Pansharpening_4bands'.

c. Select your mode in the 'Demo_Pansharpening_4bands' as described and run the demo.

（4）If you want to train your own model, compile Caffe and its interface to matlab on your device at first, then follow the steps below：

a. Put your MS and PAN training images in ./Train_MSFusion as the given files.

b. Run the 'generate_train_QBfusion'. Note that the savepath of the .h5 file should be a full path.

c. Set the full path of 'train_QBfusion.h5' in the 'train_QBfusion.txt'.

d. Set the full path of 'train_QBfusion.txt' in the 'MSDCNN_net.prototxt'. The arg 'snapshot_prefix' should also be set as a full path.

e. Locate your current path in CMD, then run the command 'caffe train -solver solver.prototxt'.

f. Get the trained model in the ./snapshots.

g. Use the trained model as instructed in ‘Demo_Pansharpening_4bands’.