Hyperspectral image classification method based on spatial-spectral convolution kernel, storage medium and equipment

Abstract

The invention discloses a hyperspectral image classification method based on a spatial-spectral convolution kernel, a storage medium and equipment. The method comprises the following steps: inputtinga three-dimensional cubic hyperspectral image; dividing a hyperspectral image data set; performing SLIC superpixel segmentation on the hyperspectral image data, and extracting a spatial convolution kernel; extracting a spectral convolution kernel; performing multiple convolution operations on the spatial convolution kernel and the spectral convolution kernel to form a spatial-spectral convolutionkernel pre-training network; and inputting the output feature map of the pre-trained network into a supervised training network for training, obtaining a cascaded supervised training network after thetraining is completed, and realizing image classification of network output features through a softmax function. According to the method, the characteristics of the images can be more effectively utilized, and rapid and high-accuracy classification of the hyperspectral images is realized.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a hyperspectral image classification method, storage medium and equipment based on a spatial spectral convolution kernel. Background technique [0002] In recent years, with the continuous development of spectral imaging technology, the analysis and processing of hyperspectral images has become one of the hot research fields of remote sensing imaging. Hyperspectral images contain hundreds of continuous spectral bands. Compared with multispectral images and panchromatic images, they can provide more and more accurate ground object information, and can more easily reveal the characteristic connections between the subtle spectra of images; but the high Spectral images themselves also have some disadvantages, such as high spectral redundancy, fewer labeled samples, longer image training time, etc. Therefore, the network for hyperspectral image classification is m...

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Problems solved by technology

However, this method also has certain shortcomings: the selection of the convolution kernel is too random, and the extracted convolution kernel may not have strong representation; the network does not make full use of the data spectral inform

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