Convolutional Neural Network (CNN) has been increasingly used for land cover mapping of remotely sensed imagery. However, large-area classification using traditional CNN is computationally expensive and produces
Unlike traditional convolutional neural networks (CNN) (Krizhevsky et al., 2012), the multiscale convolutional neural network (MCNN) is proposed in this study to extract high-level spatial features at multiple scales for classifying remote sensing images. In the MCNN, image pyramid was constructed...
Recent efforts for improving accuracies of LSM have focused on the utilization of convolutional neural network (CNN) in some image-related tasks, however, due to the inconsistency of data representation, CNN-related studies need to be further explored. In this study, a CNN-based approach for ...
Melanoma segmentation based on a convolutional neural network (CNN) has recently attracted extensive attention. However, the features captured by CNN are always local that result in discontinuous fea...
Multiscale Bidirectional Input Convolutional and Deep Neural Network for Human Activity Recognition(MBCDNN) 用于人类活动识别的多尺度双向输入卷积和深度神经网络 一些前置知识: ComplexConv1D: 复数数据:在信号处理和某些物理现象中,复数(包含实部和虚部的数)被用来表示数据。例如,在处理交流电信号或某些类型的声波时...
2.3. Multiscale Convolutional Neural Network (MSCNN) Since the input value of CNN is usually a raw signal, poor results can be obtained regardless of hyperparameter changes if there is insufficient useful information. A convolution is the most important method to analyze the signal, and the size...
In spite of the fact that convolutional neural network-based stereo matching models have shown good performance in both accuracy and robustness, the issue of image feature loss in regions of texture-less, complex scenes and occlusions remains. In this paper, we present a dense convolutional neural...
One of the earliest and most powerful DL-based convolutional neural network (CNN) models for image classification is residual networks (ResNets)31. In this study, we proposed a model by reformulating the layers as learning residual functions with reference to the layer inputs instead of learning...
Zhao and Du (2016) proposed a multiscale convolutional neural network (MCNN) to learn deep features of spatial relationships. MCNN constructs a pyramid structure from the image, which presents spatial features at different scales. The high-level spatial features are concatenated with spectral features...
Learning traffic as images: A deep convolutional neural network for large-scale transportation network speed prediction. Sensors 2017, 17, 818. [Google Scholar] [CrossRef] [Green Version] Ma, C.; Dai, G.; Zhou, J. Short-term traffic flow prediction for Urban Road sections based on time ...