deSpeckNet: Generalizing Deep Learning-Based SAR Image Despeckling

Deep learning (DL) has proven to be a suitable approach for despeckling synthetic aperture radar (SAR) images. So far, most DL models are trained to reduce speckle that follows a particular distribution, either using simulated noise or a specific set of real SAR images, limiting the applicability of these methods for real SAR images with unknown noise statistics. In this paper, we present a DL method, deSpeckNet1, that estimates the speckle noise distribution and the despeckled image simultaneously. Since it does not depend on a specific noise model, deSpeckNet generalizes well across SAR acquisitions in a variety of landcover conditions. We evaluated the performance of deSpeckNet on single polarized Sentinel-1 images acquired in Indonesia, The Democratic Republic of Congo and The Netherlands, a single polarized ALOS-2/PALSAR-2 image acquired in Japan and an Iceye X2 image acquired in Germany. In all cases, deSpeckNet was able to effectively reduce speckle and restore

[1]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[2]  Tara N. Sainath,et al.  Improving deep neural networks for LVCSR using rectified linear units and dropout , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[3]  Masoud Mahdianpari,et al.  The Effect of PolSAR Image De-speckling on Wetland Classification: Introducing a New Adaptive Method , 2017 .

[4]  Andrea Vedaldi,et al.  MatConvNet: Convolutional Neural Networks for MATLAB , 2014, ACM Multimedia.

[5]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[6]  Alfred Stein,et al.  Scattering property based contextual PolSAR speckle filter , 2017, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Leonard J. Porcello,et al.  Speckle reduction in synthetic-aperture radars , 1976 .

[8]  Jong-Sen Lee,et al.  Polarimetric SAR speckle filtering and its implication for classification , 1999, IEEE Trans. Geosci. Remote. Sens..

[9]  Florence Tupin,et al.  MuLoG, or How to Apply Gaussian Denoisers to Multi-Channel SAR Speckle Reduction? , 2017, IEEE Transactions on Image Processing.

[10]  Jong-Sen Lee,et al.  Intensity and phase statistics of multilook polarimetric and interferometric SAR imagery , 1994, IEEE Trans. Geosci. Remote. Sens..

[11]  Thomas L. Ainsworth,et al.  Improved Sigma Filter for Speckle Filtering of SAR Imagery , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Mihai Datcu,et al.  Evaluation of Bayesian Despeckling and Texture Extraction Methods Based on Gauss–Markov and Auto-Binomial Gibbs Random Fields: Application to TerraSAR-X Data , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Victor S. Frost,et al.  A Model for Radar Images and Its Application to Adaptive Digital Filtering of Multiplicative Noise , 1982, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Gabriel Vasile,et al.  Intensity-driven adaptive-neighborhood technique for polarimetric and interferometric SAR parameters estimation , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[15]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[16]  LeeJong-Sen.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980 .

[17]  Giampaolo Ferraioli,et al.  A New Ratio Image Based CNN Algorithm for SAR Despeckling , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[18]  Luisa Verdoliva,et al.  Benchmarking Framework for SAR Despeckling , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Alfred Stein,et al.  PolSARNet: A Deep Fully Convolutional Network for Polarimetric SAR Image Classification , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[21]  Henri Maître,et al.  Ratio-Based Multitemporal SAR Images Denoising: RABASAR , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[22]  Laurent Ferro-Famil,et al.  Scattering-model-based speckle filtering of polarimetric SAR data , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Florence Tupin,et al.  NL-SAR: A Unified Nonlocal Framework for Resolution-Preserving (Pol)(In)SAR Denoising , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[24]  Davide Cozzolino,et al.  SAR image despeckling through convolutional neural networks , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[25]  Samuel Foucher,et al.  Analysis, Evaluation, and Comparison of Polarimetric SAR Speckle Filtering Techniques , 2014, IEEE Transactions on Image Processing.

[26]  Gilles Aubert,et al.  A Variational Approach to Removing Multiplicative Noise , 2008, SIAM J. Appl. Math..

[27]  Dong Peng,et al.  A Filter for SAR Image Despeckling Using Pre-Trained Convolutional Neural Network Model , 2019, Remote. Sens..

[28]  Jong-Sen Lee,et al.  Digital Image Enhancement and Noise Filtering by Use of Local Statistics , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Lei Zhang,et al.  Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising , 2016, IEEE Transactions on Image Processing.

[30]  E. Nezry,et al.  Maximum A Posteriori Speckle Filtering And First Order Texture Models In Sar Images , 1990, 10th Annual International Symposium on Geoscience and Remote Sensing.

[31]  Xiaoshuang Ma,et al.  Learning a Dilated Residual Network for SAR Image Despeckling , 2017, Remote. Sens..

[32]  Matteo Matteucci,et al.  Deep Learning for SAR Image Despeckling , 2019, Remote. Sens..