Synthetic aperture radar (SAR) systems, which operate under a slant-viewing geometry, inevitably entail shadow regions in the resulting radar image. Such shadow profiles contain backprojected signatures of an object’s configuration as with target profiles; however, they are rarely utilized in current SAR-based recognition techniques. A major challenge in leveraging shadow information together lies in the intrinsic limitation of current single-pathway approaches, in which the target and shadow cannot be addressed simultaneously because of their incompatible domain properties. Hence, we herein propose novel solutions that enable the successful fusion of target and shadow regions within SAR for the first time. First, we devise new image preprocessing techniques specifically customized for shadows to compensate for their unique domain characteristics, which are distinct from the target. Second, we introduce a parallelized SAR processing mechanism such that a network can independently extract features oriented toward each conflicting modality. Third, adaptive fusion strategies are proposed for the optimal integration of features from each region while considering their relative significance layer by layer. Extensive experiments on public benchmark datasets demonstrate that the proposed framework allows a network to effectively employ shadow signatures and targets, thereby outperforming previous methods significantly for all setups.

Feature-based target detection in synthetic aperture radar (SAR) images is required for monitoring situations where it is difficult to obtain a large amount of data, such as in tactical regions. Although many features have been studied for target detection in SAR images, their performance depends on the characteristics of the images, and both efficiency and performance deteriorate when the features are used indiscriminately. In this study, we propose a two-stage detection framework to ensure efficient and superior detection performance in TSX images, using previously studied features. The proposed method consists of two stages. The first stage uses simple features to eliminate misdetections. Next, the discrimination performance for the target and clutter of each feature is evaluated and those features suitable for the image are selected. In addition, the Karhunen–Loève (KL) transform reduces the redundancy of the selected features and maximizes discrimination performance. By applying the proposed method to actual TerraSAR-X (TSX) images, the majority of the identified clusters of false detections were excluded, and the target of interest could be distinguished.

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)

Unsupervised change detection using synthetic aperture radar (SAR) imagery aims to identify changes in terrain or objects by analyzing difference images generated from multi-temporal SAR pairs. Although traditional threshold-based approaches are effective for large-scale changes, they often fail to detect small-scale changes, such as changes in vehicle positions, due to the dominance of unchanged regions in the difference image distribution. To address this issue, we propose a novel fusion-based approach designed to enhance the detectability of small objects by fusing a log-ratio image with a non-coherent change detection image using Berger's method (NCB). A fuzzification process aligns the dynamic ranges of the two images, and an adaptive fuzzy membership function derived from the statistical distribution of the log-ratio image further improves robustness. The fused difference image is generated via the Hadamard product, which reduces clutter-induced false alarms. Experimental results on both simulated and real SAR data sets demonstrate that the proposed method provides a balanced operating threshold on the fused image and achieves the best F1-score on most evaluated scenes.

한국전자파학회논문지 (The Journal of Korean Institute of Electromagnetic Engineering and Science)