Hyperspectral image classification method based on full convolution space propagation network

Abstract

The invention relates to a hyperspectral image classification method based on a full convolution space propagation network, aiming to solve the hyperspectral image classification problem and applyingthe full convolution space propagation network to hyperspectral image classification for the first time in combination with a deep learning related technology. A traditional hyperspectral image classification method based on a convolutional neural network performs pixel-by-pixel classification on images, a large number of repetitive operations exist, and the size of the input images has a great influence on a classification result. The full convolution space propagation network can receive input images of any size while reducing repetitive operation, and space information of the hyperspectralimages is fully utilized, so that high-precision classification of the hyperspectral images under a certain condition is achieved.

Description

technical field [0001] The invention relates to a hyperspectral image classification method of a full convolution space propagation network, which belongs to the field of remote sensing image processing. Background technique [0002] Hyperspectral images contain both spectral information and spatial information, and have important applications in military and civilian fields. However, the high-dimensional characteristics of hyperspectral images, high correlation between bands, and spectral mixing make hyperspectral image classification face great challenges. In recent years, with the emergence of new technologies of deep learning, the hyperspectral image classification method based on deep learning has made a breakthrough. However, deep learning models usually contain a large number of parameters and require a large number of training samples. However, there are relatively few annotated samples of hyperspectral images, and it is difficult to fully satisfy the training of d...

AI Technical Summary

Problems solved by technology

However, limited by the number of hyperspectral image training samples, the deep learning model applied in hyperspectral image classification is relatively shallow, although a large number of experiments in computer vision have shown that effectively increasing the depth is very beneficial to improve classification performance

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