光学学报, 2019, 39 (12): 1210001, 网络出版: 2019-12-06
基于神经网络的遥感图像语义分割方法 下载: 1671次
Semantic Segmentation of Remote Sensing Image Based on Neural Network
图像处理 全卷积神经网络 语义分割 双通道网络 多尺度特征 遥感图像 image processing fully convolutional neural network semantic segmentation two-channel network multiscale feature remote sensing image
摘要
为了提高遥感图像语义分割的效果和分类精度,设计了一种结合ResNet18网络预训练模型的双通道图像特征提取网络。将多重图像特征图进行拼接,融合后的特征图具有更强的特征表达能力。同时,采用批标准化层和带有位置索引的最大池化方法进一步优化网络结构,提升地表目标物的分类准确率。通过实验,将所提方法与多种神经网络方法进行准确率和Kappa系数比较。结果显示,所提的网络结构可以在小数据量样本下取得90.68%的总体准确率,Kappa系数达到了0.8595。相比其他方法,所提算法取得了更好的语义分割效果,并且整体训练时间大幅缩短。
Abstract
To improve the effect and classification accuracy of semantic segmentation of remote sensing images, a two-channel image feature extraction network combining with ResNet18 pre-training model is designed. Images with multiple features are combined, and the combined feature map has stronger ability to express features. At the same time, batch normalization layer and maximum pooling with location index are adopted to optimize the network structure and improve the classification accuracy of surface object. The accuracy and Kappa coefficient of this method are compared with those of other neural network methods by experiments. The results show that the proposed network structure achieves an overall accuracy of 90.68% when the number of data samples is small, and the Kappa coefficient reaches 0.8595. Compared with other methods, the proposed algorithm achieves better semantic segmentation effect, and greatly reduces the overall training time.
王恩德, 齐凯, 李学鹏, 彭良玉. 基于神经网络的遥感图像语义分割方法[J]. 光学学报, 2019, 39(12): 1210001. Ende Wang, Kai Qi, Xuepeng Li, Liangyu Peng. Semantic Segmentation of Remote Sensing Image Based on Neural Network[J]. Acta Optica Sinica, 2019, 39(12): 1210001.