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RSS FeedsRemote Sensing, Vol. 11, Pages 2921: HDRANet: Hybrid Dilated Residual Attention Network for SAR Image Despeckling (Remote Sensing)

 
 

6 december 2019 16:02:47

 
Remote Sensing, Vol. 11, Pages 2921: HDRANet: Hybrid Dilated Residual Attention Network for SAR Image Despeckling (Remote Sensing)
 


In order to remove speckle noise from original synthetic aperture radar (SAR) images effectively and efficiently, this paper proposes a hybrid dilated residual attention network (HDRANet) with residual learning for SAR despeckling. Firstly, HDRANet employs the hybrid dilated convolution (HDC) in lightweight network architecture to enlarge the receptive field and aggregate global information. Then, a simple yet effective attention module, convolutional block attention module (CBAM), is integrated into the proposed model to constitute a residual HDC attention block through skip connection, which further enhances representation power and performance of the model. Extensive experimental results on the synthetic and real SAR images demonstrate the superior performance of HDRANet over the state-of-the-art methods in terms of quantitative metrics and visual quality.


 
213 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2922: MASS-UMAP: Fast and Accurate Analog Ensemble Search in Weather Radar Archives (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2919: Evaluation of Five Satellite Top-of-Atmosphere Albedo Products over Land (Remote Sensing)
 
 
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