Hyperspectral image classification method based on parallel attention mechanism residual network

Abstract

The invention discloses a hyperspectral image classification method based on a parallel attention mechanism residual network. The method comprises the steps that firstly, a residual block is constructed, two parallel attention branch network branches are embedded into the residual block, and the two parallel attention branch network branches conduct recognition learning on spatial feature information and spectral feature information of input data after applying a spectral attention mechanism and a spatial attention mechanism respectively; secondly, training an input training data set by utilizing a hyperspectral image classification network formed by a plurality of constructed residual blocks which are connected in sequence; wherein the hyperspectral image classification network further comprises a 3D average pooling layer and a full connection layer which are connected in sequence, and the 3D average pooling layer is connected to the residual block adjacent to the 3D average pooling layer and used for carrying out spatial dimension adjustment on data output by the current residual block so as to reduce the calculation overhead of the whole network; and finally, inputting the feature information into a full connection layer of the hyperspectral image classification network to obtain an image classification result.

Description

technical field [0001] The invention relates to the field of remote sensing image classification, and more specifically, relates to a hyperspectral image classification method based on a residual network of a parallel attention mechanism. Background technique [0002] Hyperspectral image classification technology is a new remote sensing technology developed in the 1980s. Hyperspectral remote sensing uses many narrow electromagnetic bands to obtain image data from objects of interest. Generally, it is in the visible light, near-infrared In the range of , mid-infrared and thermal infrared bands, dozens or even hundreds of continuous bands are set, and the spectral resolution can be as high as nanometers. [0003] Most of the existing hyperspectral remote sensing image classification methods are based on two processing methods: [0004] (1) Perform dimensionality reduction processing on the spectral dimension, and input the spectral vector corresponding to a single sample into...

AI Technical Summary

Problems solved by technology

[0009] The technical problem to be solved by the present invention is to provide a kind of Hyperspectral Image Classification Method Based on Residual Network with Parallel Attention Mechanism

Images