RSC-APMN: Random Sea Condition Adaptive Perception Modulating Network for SAR-Derived Marine Aquaculture Segmentation

Abstract

The changing marine environment profoundly affects synthetic aperture radar (SAR) imaging quality. And the current deep neural network model cannot consider the variation of environmental factors. As sea conditions intensify, distinct variations in sea surface scattering characteristics emerge, leading to diverse feature distributions in images across different sea conditions, which variability poses constraints on the extraction capabilities of deep learning models. To address the above-mentioned issues, the random sea condition adaptive perception modulation network (RSC-APMN) is proposed to establish a coupled relationship with sea condition levels for adaptive enhancement of SAR imagery and semantic segmentation. Leveraging geographic coordinates and acquisition time of SAR images, RSC-APMN employs the sea condition level assessment method to estimate actual wind speeds and determine sea condition levels. The sea condition adaptive nonlinear decay module adjusts the decay ratio of different regions in SAR images under varying sea conditions based on the grayscale intensity and density characteristics of aquaculture targets, which maximizes the retention of target information while suppressing interference, such as sea clutter. To effectively utilize information from both original and enhanced images and address challenges posed by varying background noise and image blurring due to diffuse or Bragg scattering under different sea conditions, we designed the complex environments omnidirectional perception segmentation module, which ensures robust semantic segmentation in random sea conditions. Experiments demonstrate the effectiveness of the proposed approach based on marine aquaculture under sea conditions levels ranging from 0 to 5, with experimental data composed of GaoFen-3 images from Ningde, Fujian.

Keywords

• Deep learning
• marine aquaculture
• random sea conditions
• synthetic aperture radar (SAR)
• semantic segmentation