Inverse synthetic aperture radar

Radar imaging

Image (mathematics)

Compressed sensing

Geology

Mathematical analysis

Radar engineering details

Inverse problem

Superresolution

Artificial intelligence

Bandwidth (computing)

Computer vision

Mathematics

Computer science

Radar

Side looking airborne radar

Remote sensing

Telecommunications

Synthetic aperture radar

IEEE Geoscience and Remote Sensing Magazine

Synthetic aperture radar (SAR) image formation can be treated as a class of ill-posed linear inverse problems, and the resolution is limited by the data bandwidth for traditional imaging techniques via matched filter (MF). The sparse SAR imaging technology using compressed sensing (CS) has been developed for enhanced performance, such as superresolution, feature enhancement, etc. More recently, sparse SAR imaging from machine learning (ML), including deep learning (DL), has been further studied, showing great potential in the imaging area. However, there are still gaps between the two groups of methods for sparse SAR imaging, and their connections have not been established.

Sparse Synthetic Aperture Radar Imaging From Compressed Sensing and Machine Learning: Theories, applications, and trends