本文针对无人机LiDAR点云地物分类问题，提出了一种基于改进的卷积神经网络模型，通过对四川某地区采集的点云数据进行人工标注，实现了地面、建筑和植被三种地物的点云分类。利用所设计的CNN模型对人工构建的数据集进行模型训练及测试，并不断对模型参数进行优化，总体分类精度 (OA)可达93.6599%。实验结果表明，本文所设计的改进CNN能够自动学习点云的空间分布特征和形状模式，减少了人工特征设计的工作量 ...

The dilated convolution algorithm, which is widely used for image segmentation, is applied in the image classification field in this paper. In many traditional image classification algorithms, ...

For decades, scientists have looked to light as a way to speed up computing. Photonic neural networks—systems that use light instead of electricity to process information—promise faster speeds ...

Raman spectroscopy in biological applications faces challenges due to complex spectra, characterized by peaks of varying widths and significant biological background noise. Convolutional neural ...

CNN transfer learning methodology is employed by using convolutional layers to train a neural network with a training set of data containing OCT images (Wang et al., 2021). This work contributes to ...

The dilated convolution algorithm, which is widely used for image segmentation, is applied in the image classification field in this paper. In many traditional image classification algorithms, ...

In this study, we present a novel recurrence plot (RP)-based time-distributed convolutional neural network and long short-term memory (CNN-LSTM) algorithm for the integrated classification of fNIRS ...

Dr. James McCaffrey of Microsoft Research details the 'Hello World' of image classification: a convolutional neural network (CNN) applied to the MNIST digits dataset.