摘要
针对遥感图像目标密集、尺度不一、存在遮挡等特点,提出一种基于注意力机制的遥感图像分割模型用于目标分割。该模型建立在深度图像分割模型的基础上,提出在高低层特征融合之前采用通道注意力机制对低层特征进行加权处理,增强目标特征并抑制背景特征,提高信息的融合效率。为进一步增强模型对目标特征的响应能力,提出位置注意力机制对解码阶段最后的特征进行处理。最后,将加权融合后的特征图上采样到原图大小并预测像素类别。在两个遥感道路数据集上进行实验并与相关模型进行比较,结果表明所提模型在遥感影像道路提取中性能优异,可应用于复杂的遥感影像目标分割。
Aiming at the remote sensing images with target intensive,multi-scale,and occlusion,a remote sensing image segmentation model based on the attention mechanism is proposed herein.The proposed method is based on the deep image segmentation model.The channel attention mechanism is used for weighting the low-level features before high-low layer feature fusion,thus enhancing the target features,suppressing the background features,and improving the information fusion efficiency.A positional attention mechanism is proposed to process the final features of the decoding phase for further enhancing the responsiveness of the model to the target features.At last,weighted and aggregated feature maps are up-sampled to the original image size for pixel label prediction.Experiments on two remote sensing road datasets and comparisons with related models show that the proposed model displays excellent performance in remote sensing image road extraction and can be employed to complex remote sensing image segmentation.
作者
刘航
汪西莉
Liu Hang;Wang Xili(School of Computer Science,Shaanxi Normal University,Xi′an,Shaanxi 710119,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第4期162-172,共11页
Laser & Optoelectronics Progress
基金
国家自然科学基金(41471280,41561087,61701290,61701289)。
关键词
图像处理
神经网络
遥感图像
目标分割
注意力机制
imaging processing
neural network
remote sensing image
target segmentation
attention mechanism
