Search

Traditional synthetic aperture radar (SAR) image sea-land segmentation algorithms overlook the ship distribution priori-information provided by the automatic identification system (AIS) data, resulting in poor segmentation performance in complex environments such as ports, marine wetlands, beaches, and other sea-land boundaries. To address the above issues, this article comprehensively uses dual-polarization (VV and VH) SAR images and AIS data as the data source, and it specifically proposes a novel pyramid vision transformer (PVT) assisted by the long-time AIS data (AIS-PVT) for sea-land segmentation. AIS-PVT is the first attempt to integrate the ship distribution density priori-information, provided by the long-time AIS data, into the PVT network, thus the multiscale features of the sea and land can be better distinguished. In the decoding stage, we design a feature filter module (FFM). It aggregates features separately along two spatial directions from the skip connections, enhancing the representation of objects of interest while reducing the influence of redundant information. Furthermore, we develop a boundary-pixel-aware function to steer the model training process, allowing AIS-PVT to concentrate more on the neighborhood information of boundary pixels. Importantly, the AIS-PVT method captures global multiscale information and enhances the model’s data fusion capability. The conclusive experimental results demonstrate the superior performance of our approach in sea-land segmentation tasks, outperforming other state-of-the-art (SOTA) techniques.

Typical infrared polarization image fusion aims to integrate background details in the infrared intensity and salient target in the degree of linear polarization (DoLP). Many fusion methods show advanced network architecture, but few works can form effective feature representations for the differences in prior distributions of the infrared intensity and DoLP, and the interference of DoLP with noise makes fusion more challenging. This article uses a learned low-rank decomposition model to extract low-rank representations containing background details in infrared intensity and sparse features with salient targets in DoLP. To reduce noise interference, we design a fusion module based on an attention-guided filter, where the infrared intensity serves as a guide map to suppress the background in DoLP. Moreover, a novel loss constraint is proposed to improve the fusion performance. Specifically, the fusion network is trained by reconstructing polarized images in different directions from the fused image. Quantitative and qualitative experimental results validate the effectiveness of our approach. In comparison to existing methods, our fusion model can better preserve the polarization salient target and suppress the background interference with fewer parameters. The source code is available at https://github.com/lkyahpu/PIPFNet.