Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow ...Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition®. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.展开更多
海洋锋是重要的中尺度海洋现象,具有数据量小、目标小、弱边缘等特性。针对实际检测任务中弱边缘、小目标海洋锋的检测精度低、错检及漏检率高等问题,融合scSE(spatial and channel Squeeze&Excitation)空间注意力模块构建了一种改...海洋锋是重要的中尺度海洋现象,具有数据量小、目标小、弱边缘等特性。针对实际检测任务中弱边缘、小目标海洋锋的检测精度低、错检及漏检率高等问题,融合scSE(spatial and channel Squeeze&Excitation)空间注意力模块构建了一种改进的Mask R-CNN海洋锋检测模型。该方法首先对Mask R-CNN骨干网络结构进行改进,采用scSE模块引导的ResNet-50网络作为特征提取网络,通过加权策略对图像通道和空间位置进行特征突出,提升网络对重要特征的提取能力;其次,针对海洋锋目标边缘定位不准确的问题,引入IoU boundary loss构建新的Mask损失函数,提高边界检测精度。最后,为验证方法的有效性,从训练数据和实验模型上,分别设计多组对比实验。实验结果表明,相比传统Mask R-CNN、YOLOv3神经网络及现有Mask R-CNN改进网络,本文方法对SST梯度影像数据集上的强、弱海洋锋检测效果最好,定位准确率(IoU,Intersection-over-union))及检测精度(mAP,Mean Average Precision)均达0.914以上。此外,对文中设计评估模型进行检测效率实验,结果发现在不同网络模型、不同迭代次数情况下,本文提出模型消耗时间最短,远低于YOLOv3网络完成训练时所用时长。展开更多
针对卷积神经网络在高光谱图像特征提取和分类的过程中,存在空谱特征提取不充分以及网络层数太多引起的参数量大、计算复杂的问题,提出快速三维卷积神经网络(3D-CNN)结合深度可分离卷积(DSC)的轻量型卷积模型。该方法首先利用增量主成...针对卷积神经网络在高光谱图像特征提取和分类的过程中,存在空谱特征提取不充分以及网络层数太多引起的参数量大、计算复杂的问题,提出快速三维卷积神经网络(3D-CNN)结合深度可分离卷积(DSC)的轻量型卷积模型。该方法首先利用增量主成分分析(IPCA)对输入的数据进行降维预处理;其次将输入模型的像素分割成小的重叠的三维小卷积块,在分割的小块上基于中心像素形成地面标签,利用三维核函数进行卷积处理,形成连续的三维特征图,保留空谱特征。用3D-CNN同时提取空谱特征,然后在三维卷积中加入深度可分离卷积对空间特征再次提取,丰富空谱特征的同时减少参数量,从而减少计算时间,分类精度也有所提高。所提模型在Indian Pines、Salinas Scene和University of Pavia公开数据集上验证,并且同其他经典的分类方法进行比较。实验结果表明,该方法不仅能大幅度节省可学习的参数,降低模型复杂度,而且表现出较好的分类性能,其中总体精度(OA)、平均分类精度(AA)和Kappa系数均可达99%以上。展开更多
文摘针对不同磁密幅值、频率、谐波组合等复杂激励工况下磁致伸缩建模面临的精准性问题,该文利用空间注意力机制(spatial attention mechanism,SAM)对传统的卷积神经网络(convolutional neural network,CNN)进行改进,将SAM嵌套入CNN网络中,建立SAMCNN改进型网络。再结合双向长短期记忆(bidirectional long short-term memory,BiLSTM)网络,提出电工钢片SAMCNN-BiLSTM磁致伸缩模型。首先,利用灰狼优化算法(grey wolf optimization,GWO)寻优神经网络结构的参数,实现复杂工况下磁致伸缩效应的准确表征;然后,建立中低频范围单频与叠加谐波激励等复杂工况下的磁致伸缩应变数据库,开展数据预处理与特征分析;最后,对SAMCNN-BiLSTM模型开展对比验证。对比叠加3次谐波激励下的磁致伸缩应变频谱主要分量,SAMCNN-BiLSTM模型计算值最大相对误差为3.70%,其比Jiles-Atherton-Sablik(J-A-S)、二次畴转等模型能更精确地表征电工钢片的磁致伸缩效应。
文摘Aiming at the land cover (features) recognition of outdoor sports venues (football field, basketball court, tennis court and baseball field), this paper proposed a set of object recognition methods and technical flow based on Mask R-CNN. Firstly, through the preprocessing of high spatial resolution remote sensing imagery (HSRRSI) and collecting the artificial samples of outdoor sports venues, the training data set required for object recognition of land cover features was constructed. Secondly, the Mask R-CNN was used as the basic training model to be adapted to cope with outdoor sports venues. Thirdly, the recognition results were compared with the four object-oriented machine learning classification methods in eCognition®. The experiment results of effectiveness verification show that the Mask R-CNN is superior to traditional methods not only in technical procedures but also in outdoor sports venues (football field, basketball court, tennis court and baseball field) recognition results, and it achieves the precision of 0.8927, a recall of 0.9356 and an average precision of 0.9235. Finally, from the aspect of practical engineering application, using and validating the well-trained model, an empirical application experiment was performed on the HSRRSI of Xicheng and Daxing District of Beijing respectively, and the generalization ability of the trained model of Mask R-CNN was thoroughly evaluated.
文摘针对卷积神经网络在高光谱图像特征提取和分类的过程中,存在空谱特征提取不充分以及网络层数太多引起的参数量大、计算复杂的问题,提出快速三维卷积神经网络(3D-CNN)结合深度可分离卷积(DSC)的轻量型卷积模型。该方法首先利用增量主成分分析(IPCA)对输入的数据进行降维预处理;其次将输入模型的像素分割成小的重叠的三维小卷积块,在分割的小块上基于中心像素形成地面标签,利用三维核函数进行卷积处理,形成连续的三维特征图,保留空谱特征。用3D-CNN同时提取空谱特征,然后在三维卷积中加入深度可分离卷积对空间特征再次提取,丰富空谱特征的同时减少参数量,从而减少计算时间,分类精度也有所提高。所提模型在Indian Pines、Salinas Scene和University of Pavia公开数据集上验证,并且同其他经典的分类方法进行比较。实验结果表明,该方法不仅能大幅度节省可学习的参数,降低模型复杂度,而且表现出较好的分类性能,其中总体精度(OA)、平均分类精度(AA)和Kappa系数均可达99%以上。
