基于深度学习的高分辨率遥感图像海陆分割方法

Land and Sea Segmentation Method of High-Resolution Remote Sensing Image Based on Deep Learning

将高分辨率遥感图像进行像素级海陆分割是遥感应用领域的一项基础性工作,对海岸线提取和海洋近岸目标检测具有重要意义,但传统阈值方法往往由于高分辨率遥感图像覆盖范围广、地物纹理复杂等特点而难以取得预期效果。为了提升高分辨率遥感影像海陆分割精度,改善传统阈值方法的不足,基于深度神经网络模型利用编码器—解码器架构,并在编码层中引入残差块,以更好地对特征图进行高级语义信息提取,通过解码层将编码层生成的特征图还原成与输入尺寸相同的特征图,最后通过Sigmoid层对图像进行像素级海陆分割。在高分辨率遥感图像数据集上的实验结果表明,该网络模型取得良好了分割效果,准确率和Kappa系数分别达到了94.3%和93.7%。与传统方法相比,海陆分割精确度得到了有效提升。

Pixel-level sea-land segmentation of high-resolution remote-sensing images is a basic work in remote sensing applications.It is of great significance for coastline extraction and marine near-shore target detection.However,However,the traditional threshold method is often difficult to obtain the expected results due to the wide coverage of high-resolution remote sensing images and the complex texture of the ground features.In order to improve the accuracy of sea land segmentation of high-resolution remote sensing image and improve the shortcomings of traditional threshold methods,based on the depth neural network model,the encoder decoder architecture is used,and residual blocks are introduced into the coding layer to better extract the high-level semantic information of the feature map.Through the decoding layer,the feature map generated by the coding layer is restored to the feature map with the same size as the input.Finally through the Sigmoid layer,the sea land segmentation of images is made at the pixel level.The experimental results on the high-resolution remote sensing image dataset show that the network model achieves good segmentation results,and the accuracy rate and Kappa coefficient reach 94.3% and 93.7%,respectively.Compared with the existing traditional methods,this method improves the accuracy of land and sea segmentation.