Abstract:

Aiming at the problems of difficult feature extraction, low accuracy, and high false rate of high-resolution remote sensing images in target detection and segmentation, an improved Mask R-CNN convolutional neural network is proposed. This network uses ResNet50 as a feature extraction network. Based on this, it uses bottom-up and top-down two layer-hop fusion methods to perform better image feature extraction. Aiming at the problem that the size difference between different targets in the remote sensing image is too large and the target is easy to lose, an adaptive region of interest is designed to extract the region of interest. In the target segmentation, the locally fully convolved neural network is used to replace the original full convolutional neural network, and the up-sampling operation is used to replace the de-convolution operation. The verification on the NWPU VHR-10 data set shows that the method significantly improves the accuracy of multi-target detection and segmentation in remote sensing images compared with existing methods.

Key words: convolutional neural network, hierarchical hopping fusion, adaptive region of interest extraction, multi-target detection segmentation, local fusion fully connected convolutional neural network

摘要：

针对高分辨率遥感图像在目标检测与分割中特征提取困难、准确率低、虚假率高等问题，提出了一种改进的Mask R-CNN卷积神经网络。该网络以ResNet50为特征提取网络，在此基础上利用自下而上和自上而下两种分层跳连融合方式来进行更好的图像特征提取。针对遥感图像不同目标间尺寸差异过大、目标易丢失的问题，设计了自适应感兴趣区域来进行感兴趣区域提取。在目标分割中，使用局部融合全连接的卷积神经网络替换原全卷积神经网络，并使用上采样操作替换反卷积操作。在NWPU VHR-10数据集上进行验证，结果表明该方法与现有常用方法相比，显著地提高了遥感图像中多目标检测与分割的准确率。

关键词: 卷积神经网络, 分层跳连融合, 自适应感兴趣区域提取, 多目标检测分割, 局部融合全连接卷积网络