The invention relates to a dual-channel convolutional neural network (DC-CNN)-based spectral-spatial cooperative classification method for hyperspectral images. For a characteristic that hyperspectral image data adopts a three-dimensional structure, spectral-spatial features are extracted in a mode of combining a one-dimensional convolutional neural network (1D-CNN) channel with a two-dimensional convolutional neural network (2D-CNN) channel to finish the spectral-spatial cooperative classification of the hyperspectral images. For the problem of relatively little artificial mark data of the hyperspectral images, by adopting a data expansion method suitable for the hyperspectral images, the scale of training samples is increased, the training efficiency of a CNN is improved, and the over-fitting problem is reduced.

Dual-channel convolutional neural network-based spectral-spatial cooperative classification method for hyperspectral images

Provided is a hyperspectral data classification method for spectral-spatial combined data and oscillogram conversion based on convolution neural network. The invention relates to a hyperspectral data classification method for spectral-spatial combined data and oscillogram conversion, aiming to solve the problems of low data classification correct rate, long classification time, and large computational complexity in the prior art. The hyperspectral data classification method for spectral-spatial combined data and oscillogram conversion based on convolution neural network includes the following steps of: 1. normalizing the hyperspectral original data to obtain normalized hyperspectral data, and processing the normalized hyperspectral data to obtain hyperspectral spectral-spatial combined data information; and 2. converting the hyperspectral spectral-spatial combined data information into two-dimensional waveform image data. The hyperspectral data classification method is applied to the hyperspectral data classification field.

Hyperspectral data classification method for spectral-spatial combined data and oscillogram conversion based on convolution neural network

The invention provides a first-visual-angle interactive action recognition method based on global and local network fusion. The method comprises: a video is sampled to obtain different actions to obtain images and thus an action sample is formed; dimension unification processing is carried out on an action segment obtained by sampling, data enhancement is carried out, a 3D convolutional network based on a global image as an input is trained, and global spatio-temporal features of the action are learned to obtain a network classification model; a local area with significance action occurrence in the action segment is located by using a sparse optical flow; after dimension unification processing of local areas of different actions, hyperparameters of the network are adjusted, a 3D convolutional network based on a global image as an input is trained, local significant action features are learned to obtain a network classification model; and action samples are obtained by multiple samplingon the same video, statistics and ranking of the prediction numbers provided by the global and local models are carried out based on a voting method, and the type with the largest prediction numbersis used as an identified action tag.

First-visual-angle interactive action recognition method based on global and local network fusion

The invention belongs to the technical field of information processing, and discloses a three-dimensional convolutional neural network hyperspectral image classification method combining bilateral filtering. The method comprises the following steps: firstly, processing a hyperspectral image by using a bilateral filter, and then carrying out data normalization; Extracting a data block in a certainneighborhood with a to-be-classified pixel as a center from the processed hyperspectral image to serve as input end data, taking a label to which the to-be-classified pixel belongs as a label of a corresponding data block, and generating training data and test data; Constructing a three-dimensional convolutional neural network, and training and testing the three-dimensional convolutional neural network; Finally, the trained three-dimensional convolutional neural network is used to complete the space-space-space-time processing of the hyperspectral image. Spectrum joint classification. According to the invention, the space-space of the hyperspectral image is fully utilized; Spectral information improves the classification precision; A bilateral filtering algorithm is introduced, so that theclassification task accuracy is improved, the classification task time is shortened, and the time and power consumption for training a model with the same classification precision are reduced; The problems of low classification precision and long consumed time in the prior art are solved.

three-dimensional convolutional neural network hyperspectral image classification method combined with bilateral filtering

The invention relates to a 3DCNN (three-dimensional convolutional neural network)-based high-spectral image space spectrum combined classification method According to the feature that high-spectral image data is of a three-dimensional structure, the three-dimensional convolutional network suitable for a high-spectral image is constructed and high-spectral image space spectrum combined classification is completed First of all, data blocks within a certain neighborhood scope, taking a pixel to be classified as a center, are extracted from an original high-spectral image as initial space spectrum features, and through combination with a label of the pixel to be classified, the constructed 3DCNN is trained Then, by use of the trained 3DCNN, the high-spectral image space spectrum combined classification is completed The method has the following advantages: 1, the problem of need of complex processing of spectrum space dimension reduction or compression in the prior classification arts is solved; 2, the 3DCNN suitable for the high-spectral image data of the three-dimensional structure is constructed, rich information of the high-spectral image is fully utilized, and the trouble of manual set of features in advance is omitted; 3, the 3DCNN-based high-spectral image space spectrum combined classification method enlarges the application scope of depth learning and also provides a new approach for high-spectral image classification; and 4, the high-spectral image classification precision is improved

3DCNN (three-dimensional convolutional neural network)-based high-spectral image space spectrum combined classification method

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3DCNN (three-dimensional convolutional neural network)-based high-spectral image space spectrum combined classification method