A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism
A New Framework for Automatic Airports Extraction from SAR Images Using Multi-Level Dual Attention Mechanism
复制标题
使用多级双重关注机制从SAR图像中自动提取机场的新框架
DOI：
10.3390/rs12030560
复制
发表时间：
2020
期刊：
影响因子：
5
通讯作者：
Peng Zhang
中科院分区：
文献类型：
--
作者：
Lifu Chen;Siyu Tan;Zhouhao Pan;Jin Xing;Zhihui Yuan;Xuemin Xing;Peng Zhang
利用合成孔径雷达（SAR）图像检测机场在各个研究领域都具有重要意义。然而，在 SAR 图像中区分机场与周围物体具有挑战性。本文提出了一种新的框架——多级密集双重关注（MDDA）网络来提取SAR图像中的机场跑道区域（跑道、滑行道和停车场），以实现自动机场检测。该框架由三部分组成：原始SAR图像的下采样、用于特征提取和分类的MDDA网络以及机场提取结果的上采样。首先，通过下采样从高分辨率SAR图像中获取中分辨率SAR图像，以确保样本（500×500）能够包含足够的机场信息。然后将数据集输入到包含编码器和解码器的 MDDA 网络。编码器使用ResNet_101提取不同分辨率的四级特征，解码器对这些特征进行融合并进一步提取特征。解码器集成了链式残差池网络（CRP_Net）和双重注意力融合和提取（DAFE）模块。 CRP_Net模块主要使用链式残差池化和多特征融合来提取高级语义特征。在DAFE模块中，位置注意模块（PAM）和通道注意机制（CAM）与加权过滤相结合。整个解码网络采用密集连接的方式构建，增强特征之间的梯度传递，充分利用特征。最后，通过双线性插值对解码网络提取的机场结果进行上采样，完成高分辨率SAR图像的机场提取。为了验证所提出的框架，利用1 m分辨率的高分三号SAR影像进行了实验，并选择了三个不同的机场进行精度评估。结果表明，MDDA网络的平均像素精度（MPA）和平均交集比（MIoU）分别为0.98和0.97，远高于RefineNet和DeepLabV3。因此，MDDA可以实现从高分辨率SAR图像中自动提取机场，且精度令人满意。
The detection of airports from Synthetic Aperture Radar (SAR) images is of great significance in various research fields. However, it is challenging to distinguish the airport from surrounding objects in SAR images. In this paper, a new framework, multi-level and densely dual attention (MDDA) network is proposed to extract airport runway areas (runways, taxiways, and parking lots) in SAR images to achieve automatic airport detection. The framework consists of three parts: down-sampling of original SAR images, MDDA network for feature extraction and classification, and up-sampling of airports extraction results. First, down-sampling is employed to obtain a medium-resolution SAR image from the high-resolution SAR images to ensure the samples (500 × 500) can contain adequate information about airports. The dataset is then input to the MDDA network, which contains an encoder and a decoder. The encoder uses ResNet_101 to extract four-level features with different resolutions, and the decoder performs fusion and further feature extraction on these features. The decoder integrates the chained residual pooling network (CRP_Net) and the dual attention fusion and extraction (DAFE) module. The CRP_Net module mainly uses chained residual pooling and multi-feature fusion to extract advanced semantic features. In the DAFE module, position attention module (PAM) and channel attention mechanism (CAM) are combined with weighted filtering. The entire decoding network is constructed in a densely connected manner to enhance the gradient transmission among features and take full advantage of them. Finally, the airport results extracted by the decoding network were up-sampled by bilinear interpolation to accomplish airport extraction from high-resolution SAR images. To verify the proposed framework, experiments were performed using Gaofen-3 SAR images with 1 m resolution, and three different airports were selected for accuracy evaluation. The results showed that the mean pixels accuracy (MPA) and mean intersection over union (MIoU) of the MDDA network was 0.98 and 0.97, respectively, which is much higher than RefineNet and DeepLabV3. Therefore, MDDA can achieve automatic airport extraction from high-resolution SAR images with satisfying accuracy.