3D Octave卷积模型在高空间-高光谱影像分类中的应用,可以提高多树种分类任务的精度,对提高森林管理的精细化水平具有重要意义。设计了一种结合三维Octave卷积与注意力机制的3DOC-SSAM模型,通过3D Octave卷积和空间—光谱注意力机制,提...3D Octave卷积模型在高空间-高光谱影像分类中的应用,可以提高多树种分类任务的精度,对提高森林管理的精细化水平具有重要意义。设计了一种结合三维Octave卷积与注意力机制的3DOC-SSAM模型,通过3D Octave卷积和空间—光谱注意力机制,提高了模型的运行效率和分类性能。研究结果表明:①3DOC-SSAM模型总体精度达到99.53%,相对于SVM、ELM、2D-CNN、3D-CNN分别提高了13.86%、18.49%、12.90%和5.36%。且平均精度AA达到99.38%,Kappa系数达0.9947。②小样本训练的情况下,总体精度和平均精度仍然能够达到96.9%和95.52%,高于对比的模型。研究结果为多树种分类任务提供了一个高效且高精度的解决方案,在林业遥感中的应用前景广阔,有助于提升森林资源管理的科学性和可持续性。展开更多
Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine ...Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.展开更多
GNU Octave是一款数值计算软件,具有免费、开源以及几乎完全兼容MATLAB语言的优点。然而,Octave内置的基于LLVM的实验性即时编译器仅支持对少部分代码进行即时编译,无法有效解决Octave效率低下的问题。基于Octave即时编译器探究对Octav...GNU Octave是一款数值计算软件,具有免费、开源以及几乎完全兼容MATLAB语言的优点。然而,Octave内置的基于LLVM的实验性即时编译器仅支持对少部分代码进行即时编译,无法有效解决Octave效率低下的问题。基于Octave即时编译器探究对Octave的性能优化方案,从工作原理角度出发,对该即时编译器整体工作原理和其中的类型推断系统进行分析;从工作现状角度出发,评估该即时编译器对Octave代码的适用范围和性能提升效果;针对该即时编译器的内置函数调用、索引运算与算术逻辑运算进行特性修复和功能新增,使Octave获得性能提升。实验结果表明,基于即时编译器的优化方案有效扩展了即时编译器的适用范围,为Octave代码执行带来56~283倍不等的性能提升。此外,总结了该即时编译器中存在的16类缺陷,对进一步优化Octave性能具有参考意义。展开更多
基金supported in part by the National Natural Foundation of China(No.62176147)。
文摘Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.
