Mangrove monitoring and extraction based on multi-source remote sensing data: a deep learning method based on SAR and optical image fusion

Mangroves are indispensable to coastlines, maintaining biodiversity, and mitigating climate change. Therefore, improving the accuracy of mangrove information identification is crucial for their ecological protection. Aiming at the limited morphological information of synthetic aperture radar (SAR) images, which is greatly interfered by noise, and the susceptibility of optical images to weather and lighting conditions, this paper proposes a pixel-level weighted fusion method for SAR and optical images. Image fusion enhanced the target features and made mangrove monitoring more comprehensive and accurate. To address the problem of high similarity between mangrove forests and other forests, this paper is based on the U-Net convolutional neural network, and an attention mechanism is added in the feature extraction stage to make the model pay more attention to the mangrove vegetation area in the image. In order to accelerate the convergence and normalize the input, batch normalization (BN) layer and Dropout layer are added after each convolutional layer. Since mangroves are a minority class in the image, an improved cross-entropy loss function is introduced in this paper to improve the model's ability to recognize mangroves. The AttU-Net model for mangrove recognition in high similarity environments is thus constructed based on the fused images. Through comparison experiments, the overall accuracy of the improved U-Net model trained from the fused images to recognize the predicted regions is significantly improved. Based on the fused images, the recognition results of the AttU-Net model proposed in this paper are compared with its benchmark model, U-Net, and the Dense-Net, Res-Net, and Seg-Net methods. The AttU-Net model captured mangroves' complex structures and textural features in images more effectively. The average OA, F1-score, and Kappa coefficient in the four tested regions were 94.406%, 90.006%, and 84.045%, which were significantly higher than several other methods. This method can provide some technical support for the monitoring and protection of mangrove ecosystems.