Precise Cross-Range Scaling for ISAR Images Using Feature Registration

Abstract

This letter proposes a precise cross-range scaling algorithm for inverse synthetic aperture radar (ISAR) images by estimating the effective rotation angle through coordinate locations of feature points extracted from two sequenced subaperture ISAR images. In the approach, we first extract adequate feature points and feature descriptor vectors from these two images by scale-invariant feature transform and speeded-up robust features. Then, a two-stage registering scheme is employed to match these feature points to link the two images. Consequently, the effective rotation angle is efficiently and robustly estimated by evaluating a cost function based on the coordinate locations of the matched feature points. Experiments of simulated and real signals validate this proposal.