Reverberation Suppression and Multilayer Context Aware for Underwater Forward-Looking Sonar Image Segmentation

Forward-looking sonar (FLS) imaging systems can provide long-range underwater environments in ultralow visibility and contrast, holding a critical role in the recognition and segmentation of underwater objects. However, sonar image segmentation presents significant challenges due to the main factors: 1) the reverberation strength increased by the coherent scattering phenomenon, which hampers the segmentor’s ability to capture object features accurately; and 2) insufficient multifeature utilization of weakly annotated samples and a lack of prior information on underwater object scales. To overcome this challenge, we proposed a novel FLS image segmentation method based on the reverberation suppression and multilayer context aware. Detailed statistical analysis of reverberation distribution using nonlinear least squares fitting, we design a self-supervised background suppression auxiliary branch (BSAB) to reduce inherent gamma noise and preserve pure features. To further leverage the multiscale context features extracted from different transformer encoding layers, we propose a multilayer aggregation transformer (MLAT) as an encoder to acquire more comprehensive target features. In addition, a global-local feature complementary fusion mechanism was designed for the interaction of these independent features, to achieve an optimal comprehensive state at the spatial and channel level. The comparative experiments demonstrate that FISNet achieves superior performance with state-of-the-art segmentation networks.