Radar imaging

SIGNAL (programming language)

Interference (communication)

Biology

Identification (biology)

Statistics

Kurtosis

Telecommunications

Inverse synthetic aperture radar

Computer science

Botany

Constant false alarm rate

Geology

Pattern recognition (psychology)

Mathematics

Synthetic aperture radar

Artificial intelligence

Remote sensing

Programming language

Channel (broadcasting)

False alarm

Radar

Computer vision

IEEE Transactions on Geoscience and Remote Sensing

Recent studies have shown that complex information in single-channel synthetic aperture radar (SAR) imagery has practically always been underrated. This improves the perception of their potential for ocean monitoring. Based on the in-depth interpretation of complex signal kurtosis (CSK), this paper proposes a new ship detection method based on CSK in single-channel SAR imagery. The proposed method consists of two main parts, i.e., region proposal and target identification. The basic idea is to first detect potential ship locations based on the region proposal. Then, the final ship target is acquired based on the target identification. Compared to conventional methods based on detected products, e.g., the constant false alarm rate (CFAR), the proposed method has three advantages. First, CSK can take advantage of both non-Gaussianity and noncircularity, which is the fundamental concept distinguishing complex signal analysis from the real case. Second, the proposed method can be intrinsically free of false alarms caused by radio frequency interference (RFI). Finally, the proposed method can avoid missing detection in dense target situations. This methodology has been demonstrated over significant data sets acquired from Sentinel-1, TerraSAR-X, and Gaofen-3. These results validate that CSK is a vital indicator of ship detection. Complex information is expected to play a more important role in single-channel SAR imagery.

Ship Detection Based on Complex Signal Kurtosis in Single-Channel SAR Imagery