Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road netw...Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.展开更多
文摘Due to the complicated background of objectives and speckle noise, it is almost impossible to extract roads directly from original synthetic aperture radar(SAR) images. A method is proposed for extraction of road network from high-resolution SAR image. Firstly, fuzzy C means is used to classify the filtered SAR image unsupervisedly, and the road pixels are isolated from the image to simplify the extraction of road network. Secondly, according to the features of roads and the membership of pixels to roads, a road model is constructed, which can reduce the extraction of road network to searching globally optimization continuous curves which pass some seed points. Finally, regarding the curves as individuals and coding a chromosome using integer code of variance relative to coordinates, the genetic operations are used to search global optimization roads. The experimental results show that the algorithm can effectively extract road network from high-resolution SAR images.
文摘针对无人机(UAV)影像中道路小目标漏检和目标检测精度低、鲁棒性差等问题,设计一种基于全局特征提取的UAV道路病害检测算法GFE-RDD(Global Feature Extraction-Road Disease Detection)。将卷积神经网络(CNN)与Transformer融合的GFE-Transformer模块嵌入主干网络,提升捕获长距离依赖关系的能力以获得全局上下文信息。为了更好地检测出小目标的道路病害,提出一个融合高效双通道注意力机制(EDA)的小目标检测头。另外,采用WIoUv3(Wise-Intersection over Union vision 3)作为网络的损失函数,解决训练数据中锚框质量差异较大的问题,并提高检测的准确性。在自制的道路多病害数据集上的实验结果表明,所提算法在道路病害检测任务中的F1分数达到0.765,mAP50达到0.796,均高于DETR(DEtection TRansformer)等当前主流算法,取得了较高的检测准确率。
