NLSAN: A Non-Local Scene Awareness Network for Compact Polarimetric ISAR Image Super-Resolution

• Published in: IEEE transactions on geoscience and remote sensing Vol. 61; pp. 1 - 16
• Main Authors: Li, Ming-Dian, Deng, Jun-Wu, Xiao, Shun-Ping, Chen, Si-Wei
• Format: Journal Article
• Language: English
• Published: New York IEEE 2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Compact polarimetric (CP); Deep learning; Image enhancement; Image resolution; Interpolation; Inverse synthetic aperture radar; inverse synthetic aperture radar (ISAR); Manganese; Penalty function; Performance indices; Polarimetry; Polarization; Receiving antennas; SAR (radar); Satellite imagery; scene awareness; Signal to noise ratio; Space surveillance; space target; Space vehicles; super-resolution; Superresolution; Transmitting antennas
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3331822

Polarimetric inverse synthetic aperture radar (ISAR) can operate all-day and all-weather, making it crucial for space surveillance. The compact polarimetric (CP) mode balances hardware complexity and polarimetric information, which is commonly equipped with ISAR systems. Given the constraints of limited physical conditions, exploring ISAR image super-resolution is worthwhile. Currently, deep learning models have been employed for enhancing ISAR image super-resolution. However, the super-resolution performance is limited by local interpolation and the occurrence of artifacts. To address these limitations, this work presents a non-local scene awareness network (NLSAN), which incorporates a non-local interpolation approach to capture global textures. Furthermore, a scene awareness scheme is established by integrating semantic and super-resolution information, concerning the varying levels of artifacts in different regions. The training process can be regulated by a designed penalty function to mitigate potentially generated artifacts. A dataset of CP ISAR images of satellite targets is constructed for comparison analysis. The proposed NLSAN method yields more elaborate super-resolution results with fewer artifacts. Quantitative evaluations are also carried out using global and local indexes, such as the peak-signal-to-noise ratio (PSNR), the image entropy, and the 3 dB width of strong scatters. Compared with the typical state-of-the-art methods, the proposed approach achieves superior super-resolution performance, with an overall performance improvement of at least 9.2% and enhanced generalization capabilities.