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Learned SAR Speckle Filter: Despeckling Synthetic-Aperture Radar images using a Deep Residual Convolutional Neural Network

Tim Davis, Vinit Jain

Abstract:

Synthetic Aperture Radar (SAR) images suffer from the effects of speckle noise which arises from coherent summation of the signals scattered from ground scatters distributed randomly within each pixel. Speckling affects the human- and machine-readability of the images. Here, we propose to use deep learning to train a convolutional neural network model that outputs a despeckled SAR image. Convolutional Neural network architectures such as Noise2Noise and Noise2Void have been proven successful when the ground truth of the input image is not available. Hence, the primary goal of this research project is to train a deep residual CNN to ameliorate speckling in SAR images based on noisy input only. Lee and BM3D filters were evaluated to compare the results. The CNN models performed comparably to the filters in terms when evaluating against quasi-ground-truth images, and significantly outperformed the filters in terms of processing efficiency.

<p align="center">SAR image before and after despeckling </p>

Principal components

Pipeline

Please note that all code was written for and tested in Python3.

filter.py

This is the complete pipeline for processing raw SAR inputs into despeckled intensity images using a pretrained model.

It depends on tif2intensity.py for converting the raw SAR input into a (speckled) intensity image, predictor.py for predicting despeckled image patchs, and model.util for helper functions.

usage: filter.py [-h] -o OUTPUT [--channels_last] -m MODEL
                 [--mean_correction MEAN_CORRECTION] [--no_weighting] [-r]                             
                 [-s STRIDE] [--single_channel_output]                                                 
                 input                                                                                 
                                                                                                       
positional arguments:
  input                 tif image or directory of tif images

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        output file or directory
  --channels_last       indicate if the image channels are stored last
  -m MODEL, --model MODEL
                        which model to use for processing [lin|log]
  --mean_correction MEAN_CORRECTION
                        when to apply mean-correction to images
                        [smart|always|never]
  --no_weighting        do not center-weight image patches
  -r                    recursively process subdirectories
  -s STRIDE, --stride STRIDE
                        stride when processing images (default 128)
  --single_channel_output
                        create a separate file for each channel (polarity)

Example usage with provided sample.tif

python3 filter.py -o result.tif -m lin --single_channel_output ./images/sample.tif

tif2intensity.py

These are helper functions for turning a raw SAR image into a (speckled) intensity image.

predictor.py

This is a class for making predictions using specified weights.

Training

Dataset generation

The model was trained using 2-channel intensity images, with the first channel for the VH intensity image and the second channel for the VV intensity image. These 2-channel intensity images were created from 4-channel raw SAR images using tif2intensity.py.

The data must be batched into sets of identical views, for which batcher.py was used. This takes a .csv file as input that indicates to which set each image belongs (see example below) and batches each set into its own directory. Set number 0 is reserved for "bad" images that are either in an incorrect format or do not fit into any other set. These will be ignored by the batcher. Negative numbered sets are validation images and will be excluded from the training data by model.pairgenerator.py during training, but still copied to the output directory for later use in validation.

train.py

Actual training of the model is handled by train.py. It depends on model.py for the model specification, pairgenerator.py for loading the dataset, and util.py for helper functions

usage: train.py [-h] --image_dir IMAGE_DIR [--batch_size BATCH_SIZE]
                [--nb_epochs NB_EPOCHS] [--lr LR] [--steps STEPS]
                [--weight WEIGHT] [--output_path OUTPUT_PATH] [--model MODEL]
                [--min_date_separation MIN_DATE_SEPARATION]
                [--logspace LOGSPACE]

train noise2noise model

optional arguments:
  -h, --help            show this help message and exit
  --image_dir IMAGE_DIR
                        image dir for input and target values (default: None)
  --batch_size BATCH_SIZE
                        batch size (default: 32)
  --nb_epochs NB_EPOCHS
                        number of epochs (default: 20)
  --lr LR               learning rate (default: 0.01)
  --steps STEPS         steps per epoch (default: 32)
  --weight WEIGHT       weight file for restart (default: None)
  --output_path OUTPUT_PATH
                        checkpoint dir (default: checkpoints)
  --model MODEL         model architecture (default: red30)
  --min_date_separation MIN_DATE_SEPARATION
                        Minimum date between image pair acquisition (default:
                        6)
  --logspace LOGSPACE   Convert images to logspace before training (default:
                        False)

The following command was used for linear-space training:

python ./model/train.py --image_dir ./vhvv_sets/ --batch_size 16 --nb_epochs 1000 --lr 0.001 --steps 32 --output_path ./checkpoints_vhvv_lin/ --min_date_separation 90

The following command was used for logarithmic-space training:

python ./model/train.py --image_dir ./vhvv_sets/ --batch_size 16 --nb_epochs 1000 --lr 0.001 --steps 32 --output_path ./checkpoints_vhvv/ --min_date_separation 90 --logspace

model.py

This contains the specification for the architecture of the model.

pairgenerator.py

This contains the imgloader class, which extends keras.utils.Sequence and is used for loading the dataset during training. All loading is done through on-the-fly loading of randomly selected, matching image patches.

Evaluation

Evaluation of the results was done using compare_psnr and compare_ssim from skimage.measure. All images for two volcanoes (Ertaale and Pitonfournaise) were either dedicated to evaluation sets or excluded from training set, so that no knowledge of these specific volcanoes was acquired by the CNN during the training process.

Lee, Kuan, and Frost filters were taken from the PyRadar package. BM3D filtering was done using the PyBM3D package.