Multi-modal remote sensing data classification method based on graph convolutional network

Abstract

The invention provides a multi-modal data image fusion classification method based on a graph convolutional network, and mainly solves the problem that the existing hyperspectral image classification precision is low. According to the implementation scheme, a hyperspectral image data set is obtained; performing multi-source data fusion on the original HSI and LiDAR images in a GS fusion mode to obtain a hyperspectral image GS fusion image; respectively extracting invariant attribute features of the HSI image and the LiDAR image, and obtaining an invariant attribute feature fusion image through a feature fusion mode based on a weighted fusion image; inputting the hyperspectral image GS fusion image and the invariant attribute feature fusion result into miniGCN and 2DCNN branches to further extract spectral features and spatial features; carrying out feature fusion on the extracted spectral features and spatial features through a multiplicative fusion strategy; and classifying multiplicative feature fusion results through a classification network. The method reduces the loss of feature information, improves the classification performance, and can be used for hyperspectral image classification.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to a multimodal remote sensing data classification method, which can be used for hyperspectral image classification. Background technique [0002] With the continuous development of image classification technology, remote sensing image classification plays an increasingly important role. Urban planning, land detection, vegetation classification, etc. all depend on the results of material classification in a specific area. Hyperspectral image HSI contains rich spectral information, which can be used to observe and classify ground object information, but HSI cannot distinguish the material categories composed of the same material. This shows that in some specific scenarios, single source image is not conducive to classification, while remote sensing multi-source image classification is gradually applied to material classification. LiDAR image LiDAR contains the spatia...

AI Technical Summary

Problems solved by technology

However, this method is only aimed at a single HSI classification task, and does not involve the classification application of multi-source images, so it is difficult to achieve high-precision classification results

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