ISAR Weak Feature Enhancement With Perturbation Defense Using Hybrid Clustering Oversegmentation

Weak feature enhancement is an important and intractable issue in machine vision fields. In this paper, an effective complex-scene inverse synthetic aperture radar (ISAR) image feature enhancement method is proposed based on spatial neighborhood sparse constraint hybrid clustering oversegmentation (SNSC-HCOS). A hybrid superpixel concept is first developed considering the consistency of pixel labels and the discrimination of perturbations. The scattering characteristic similarity, spatial proximity, neighborhood information, sparsity bias, and overall image features are simultaneously explored. The SNSC-HCOS algorithm is designed based on the hybrid superpixel concept, data reconstruction, and fuzzy c-means (FCM) clustering. Both sparsity constraints and local spatial information constraints are embedded into the oversegmentation framework, improving the perturbation defense capability and clustering robustness. To correct misclassified pixel membership and improve system efficiency, median membership filtering and area merging strategies are further applied. Extensive qualitative and quantitative experiments are conducted on a real-scene dataset, demonstrating that SNSC-HCOS greatly outperforms the outstanding comparison systems. The proposed system has good segmentation performance, can generate more pure superpixels with regular shapes and uniform sizes, and has perturbation defense and feature enhancement capabilities.