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Abstract:A few previous studies have illustrated the potentials of compact polarimetric Synthetic Aperture Radar (CP SAR) in ship detection. In this paper, we design a ship detection algorithm of CP SAR from the perspective of computer vision. A ship detection algorithm using the pulsed cosine transform (PCT) visual attention model is proposed to suppress background clutter and highlight conspicuous ship targets. It is the first time that a visual attention model is introduced to CP SAR application. The proposed algorithm is a quick and complete framework for practical use. Polarimetric features-the relative phase δ and volume scattering component-are extracted from m-δ decomposition to eliminate false alarms and modify the PCT model. The constant false alarm rate (CFAR) algorithm based on lognormal distribution is adopted to detect ship targets, after a clutter distribution fitting procedure of the modified saliency map. The proposed method is then tested on three simulated circular-transmit-linear-receive (CTLR) mode images, which covering East Sea of China. Compared with the detection results of SPAN and the saliency map with only single-channel amplitude, the proposed method achieves the highest detection rates and the lowest misidentification rate and highest figure of merit, proving the effectiveness of polarimetric information of compact polarimetric SAR ship detection and the enhancement from the visual attention model.

Although unsupervised representation learning (RL) can tackle the performance deterioration caused by limited labeled data in synthetic aperture radar (SAR) object classification, the neglected discriminative detailed information and the ignored distinctive characteristics of SAR images can lead to performance degradation. In this paper, an unsupervised multi-scale convolution auto-encoder (MSCAE) was proposed which can simultaneously obtain the global features and local characteristics of targets with its U-shaped architecture and pyramid pooling modules (PPMs). The compact depth-wise separable convolution and the deconvolution counterpart were devised to decrease the trainable parameters. The PPM and the multi-scale feature learning scheme were designed to learn multi-scale features. Prior knowledge of SAR speckle was also embedded in the model. The reconstruction loss of the MSCAE was measured by the structural similarity index metric (SSIM) of the reconstructed data and the images filtered by the improved Lee sigma filter. A speckle suppression restriction was also added in the objective function to guarantee that the speckle suppression procedure would take place in the feature learning stage. Experimental results with the MSTAR dataset under the standard operating condition and several extended operating conditions demonstrated the effectiveness of the proposed model in SAR object classification tasks.

Built-up area (BA) extraction using synthetic aperture radar (SAR) data has emerged as a potential method in urban research. Currently, typical deep-learning-based BA extractors show high false-alarm rates in the layover areas and subsurface bedrock, which ignore the surrounding information and cannot be directly applied to large-scale BA mapping. To solve the above problems, a novel transformer-based BA extraction framework for SAR images is proposed. Inspired by SegFormer, we designed a BA extractor with multi-level dual-attention transformer encoders. First, the hybrid dilated convolution (HDC) patch-embedding module keeps the surrounding information of the input patches. Second, the channel self-attention module is designed for dual-attention transformer encoders and global modeling. The multi-level structure is employed to produce the coarse-to-fine semantic feature map of BAs. About 1100 scenes of Gaofen-3 (GF-3) data and 200 scenes of Sentinel-1 data were used in the experiment. Compared to UNet, PSPNet, and SegFormer, our model achieved an 85.35% mean intersection over union (mIoU) and 94.75% mean average precision (mAP) on the test set. The proposed framework achieved the best results in both mountainous and plain terrains. The experiments using Sentinel-1 shows that the proposed method has a good generalization ability with different SAR data sources. Finally, the BA map of China for 2020 was obtained with an overall accuracy of about 86%, which shows high consistency with the global urban footprint. The above experiments proved the effectiveness and robustness of the proposed framework in large-scale BA mapping.