Zhang Jiahao, Yang Pengju, Ren Xincheng
School of Physics and Electronic Information, Yan'an University, Yan'an 716000, China.
Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China.
Sensors (Basel). 2024 Aug 23;24(17):5460. doi: 10.3390/s24175460.
Oil spill SAR images are characterized by high noise, low contrast, and irregular boundaries, which lead to the problems of overfitting and insufficient capturing of detailed features of the oil spill region in the current method when processing oil spill SAR images. An improved DeepLabV3+ model is proposed to address the above problems. First, the original backbone network Xception is replaced by the lightweight MobileNetV2, which significantly improves the generalization ability of the model while drastically reducing the number of model parameters and effectively addresses the overfitting problem. Further, the spatial and channel Squeeze and Excitation module (scSE) is introduced and the joint loss function of Bce + Dice is adopted to enhance the sensitivity of the model to the detailed parts of the oil spill area, which effectively solves the problem of insufficient capture of the detailed features of the oil spill area. The experimental results show that the mIOU and F1-score of the improved model in an oil spill region in the Gulf of Mexico reach 80.26% and 88.66%, respectively. In an oil spill region in the Persian Gulf, the mIOU and F1-score reach 81.34% and 89.62%, respectively, which are better than the metrics of the control model.
溢油合成孔径雷达（SAR）图像具有高噪声、低对比度和边界不规则的特点，这导致在当前处理溢油SAR图像的方法中出现过拟合问题，并且对溢油区域细节特征的捕捉不足。为了解决上述问题，提出了一种改进的深度卷积神经网络（DeepLabV3+）模型。首先，用轻量级的MobileNetV2替换原始的骨干网络Xception，这在显著减少模型参数数量的同时，大幅提高了模型的泛化能力，并有效解决了过拟合问题。此外，引入了空间和通道挤压与激励模块（scSE），并采用Bce+Dice联合损失函数，以增强模型对溢油区域细节部分的敏感性，有效解决了溢油区域细节特征捕捉不足的问题。实验结果表明，改进后的模型在墨西哥湾溢油区域的平均交并比（mIOU）和F1分数分别达到80.26%和88.66%。在波斯湾溢油区域，mIOU和F1分数分别达到81.34%和89.62%，优于对照模型的指标。
