The invention relates to the field of deep learning and image classification processing of artificial intelligence, in particular to ground object classification of a hyperspectral image by using a deep convolutional neural network. In a model training phase, the method comprises the following steps: firstly, converting a to-be-classified hyperspectral image into a single-pixel multi-dimensional image with a label; extracting a feature map of a single pixel based on a hole convolution combination model, constructing a convolutional neural network with three convolution layers and three activation layers to extract main features, and finally, using a layer of full connection and a Softmax function as a classifier to complete single pixel classification. The spectral information of the hyperspectral remote sensing image is fully utilized; the hyperspectral remote sensing image classification method based on the deep hole convolution neural network solves the gridding problem in hole convolution, enlarges the convolution receptive field, improves the convolution efficiency, reduces the parameter quantity, improves the classification precision, and is accurate to pixels, efficient andlightweight.
Hyperspectral remote sensing image classification method using hole convolution