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

Abstract

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.

Description

technical field [0001] The invention belongs to the technical field of remote sensing information processing, and relates to a hyperspectral image classification method, in particular to a hyperspectral image classification method based on dual-channel convolutional neural network space-spectrum union. Background technique [0002] Hyperspectral remote sensing images have high spectral resolution, multiple imaging bands, and large amounts of information, and are widely used in remote sensing applications. Hyperspectral image classification technology is an important part of hyperspectral image processing technology. It mainly includes two steps of feature extraction and classification. Among them, features are extracted from the original hyperspectral image. This step has a great impact on the classification accuracy of hyperspectral images: if The robustness of the extracted classification features can greatly improve the classification accuracy; on the contrary, the classi...

AI Technical Summary

Problems solved by technology

At the same time, a data expansion method is proposed to overcome the problem of over-fitting when hyperspectral images are manually marked with less data.

Images