Cloud-Net: A semantic segmentation CNN for cloud detection

Cloud-Net is an end-to-end cloud detection algorithm for Landsat 8 imagery. However, it can be used for other semantic segmentation applications, too. It gets a four-channel RGBNir image of Landsat 8 and predicts the location of clouds.

Cloud-Net has been introduced in the following IGARSS paper:

S. Mohajerani and P. Saeedi. "Cloud-Net: An End-to-end Cloud Detection Algorithm for Landsat 8 Imagery". (forthcoming) 2019, to appear at IEEE International Geoscience and Remote Sensing Symposium (IGARSS). URL: https://arxiv.org/pdf/1901.10077.pdf

Cloud-Net is a modification of CPAdv-Net, which is proposed in the following paper:

S. Mohajerani and P. Saeedi, "Shadow Detection in Single RGB Images Using a Context Preserver Convolutional Neural Network Trained by Multiple Adversarial Examples," in IEEE Transactions on Image Processing, vol. 28, no. 8, pp. 4117-4129, Aug. 2019. doi: 10.1109/TIP.2019.2904267, URL: https://ieeexplore.ieee.org/document/8664462

Training Cloud-Net on 38-Cloud Training Set

Requirements

The network has been tested with the following setup:
Windows 10, CentOS Linux release 7.5.1804
Python 3.6
Tensorflow 1.9.0, 1.10.0, 1.12.0
Keras 2.2.4
Scikit-image 0.15.0

Scripts

Run python main_train.py to train the network on 38-Cloud training set. The path to the dataset folder should be set at GLOBAL_PATH = 'path to 38-cloud dataset'. The directory tree for the dataset looks like as following:

├──38-Cloud dataset

│------------├──Cloud-Net_trained_on_38-Cloud_training_patches.h5

│------------├──Training

│------------------├──Train blue
. . .

│------------------├──training_patches_38-cloud.csv

│------------├──Test

│------------------├──Test blue
. . .

│------------------├──test_patches_38-cloud.csv

│------------├──Predictions

The training patches are resized to 192 192 before each iteration. Then, four corresponding spectral bands are stacked together to create a 192 192 * 4 array. A .log file is generated to keep track of the loss values. The loss function used for training is the soft Jaccard loss.

Testing Cloud-Net on 38-Cloud Test Set

Run python main_test.py for getting the predictions. The weights of Cloud-Net, pretrained on 38-Cloud training set, is available here: Cloud-Net_trained_on_38-Cloud_training_patches.h5. Relocate this file in the dataset directory as shown above. The predicted cloud masks will be generated in the "Predictions" folder. Then, use the Evaluation over 38-Cloud Dataset section to get the numerical results and precited cloud masks for the entire scenes.