Deep Learning for SAR Image Despeckling

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

[2]  Alexander A. Sawchuk,et al.  Adaptive Noise Smoothing Filter for Images with Signal-Dependent Noise , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Zhenghao Shi,et al.  A comparison of digital speckle filters , 1994, Proceedings of IGARSS '94 - 1994 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Pascal Vincent,et al.  Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion , 2010, J. Mach. Learn. Res..

[5]  José M. Bioucas-Dias,et al.  Multiplicative Noise Removal Using Variable Splitting and Constrained Optimization , 2009, IEEE Transactions on Image Processing.

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

[7]  E. Nezry,et al.  Adaptive speckle filters and scene heterogeneity , 1990 .

[8]  Gabriele Steidl,et al.  Removing Multiplicative Noise by Douglas-Rachford Splitting Methods , 2010, Journal of Mathematical Imaging and Vision.

[9]  Shawn D. Newsam,et al.  Bag-of-visual-words and spatial extensions for land-use classification , 2010, GIS '10.

[10]  J. Goodman.  Some fundamental properties of speckle , 1976 .

[11]  Bo Du,et al.  Deep Learning for Remote Sensing Data: A Technical Tutorial on the State of the Art , 2016, IEEE Geoscience and Remote Sensing Magazine.

[12]  Fabrizio Argenti,et al.  Speckle removal from SAR images in the undecimated wavelet domain , 2002, IEEE Trans. Geosci. Remote. Sens..

[13]  Haokui Zhang,et al.  Deep learning for remote sensing image classification: A survey , 2018, WIREs Data Mining Knowl. Discov..

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

[15]  R. Bamler,et al.  Synthetic aperture radar interferometry , 1998 .

[16]  Florence Tupin,et al.  Iterative Weighted Maximum Likelihood Denoising With Probabilistic Patch-Based Weights , 2009, IEEE Transactions on Image Processing.

[17]  Zhenfeng Shao,et al.  PatternNet: A Benchmark Dataset for Performance Evaluation of Remote Sensing Image Retrieval , 2017, ISPRS Journal of Photogrammetry and Remote Sensing.

[18]  Yonghong Zhang,et al.  Study on the comparison of the land cover classification for multitemporal MODIS images , 2008, 2008 International Workshop on Earth Observation and Remote Sensing Applications.

[19]  Jeng-Shyang Pan,et al.  Remote sensing image object recognition based on convolutional neural network , 2017, 2017 First International Conference on Electronics Instrumentation & Information Systems (EIIS).

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

[21]  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.

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

[23]  Jean-Michel Morel,et al.  A non-local algorithm for image denoising , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[24]  Claudio Prati,et al.  A New Algorithm for Processing Interferometric Data-Stacks: SqueeSAR , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[25]  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.

[26]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

[27]  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.

[28]  Jianing Shi,et al.  A Nonlinear Inverse Scale Space Method for a Convex Multiplicative Noise Model , 2008, SIAM J. Imaging Sci..

[29]  Roberto Cipolla,et al.  SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[30]  Luisa Verdoliva,et al.  Multitemporal SAR Image Despeckling Based on Block-Matching and Collaborative Filtering , 2017, IEEE Transactions on Geoscience and Remote Sensing.

[31]  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.

[32]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[33]  Johannes R. Sveinsson,et al.  SAR image denoising using total variation based regularization with sure-based optimization of the regularization parameter , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[34]  Fawwaz T. Ulaby,et al.  SAR speckle reduction using wavelet denoising and Markov random field modeling , 2002, IEEE Trans. Geosci. Remote. Sens..

[35]  Torbjørn Eltoft,et al.  Homomorphic wavelet-based statistical despeckling of SAR images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[36]  Iasonas Kokkinos,et al.  DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs , 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[37]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

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

[39]  Luisa Verdoliva,et al.  A Nonlocal SAR Image Denoising Algorithm Based on LLMMSE Wavelet Shrinkage , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[40]  Yao Zhao,et al.  Adaptive Total Variation Regularization Based SAR Image Despeckling and Despeckling Evaluation Index , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[41]  L. Rudin,et al.  Nonlinear total variation based noise removal algorithms , 1992 .

[42]  X. Chen,et al.  RAPID TARGET DETECTION IN HIGH RESOLUTION REMOTE SENSING IMAGES USING YOLO MODEL , 2018 .