A Classification Method for High Resolution Remote Sensing Image Based on Parallel Hybrid Convolutional Network

Abstract

The invention discloses a high-resolution remote sensing image classification method based on a parallel hybrid convolutional network. The specific steps are: inputting high-resolution remote sensing images and corresponding sample label data, including training sample data sets and test sample data sets; Build 3D convolutional neural network and 2D convolutional neural network, build information fusion transformation network to achieve spatial spectral feature information fusion and deep extraction; batch input training sample datasets to train the network, construct cross entropy loss function and stochastic gradient descent algorithm optimization Network and update parameters until the network converges; input the test sample data set into the hybrid network model, output the predicted value of the test sample label, and complete the high-scoring image classification. The invention can simultaneously extract the spatial features and spectral features of high-resolution remote sensing images, perform feature fusion to achieve high-efficiency and high-precision classification of images, and provide an important role in natural resource monitoring, geographic and national census, urban planning, climate change and other researches.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a high-resolution remote sensing image classification method based on a parallel hybrid convolution network. Background technique [0002] With the rapid development of remote sensing satellite technology, the resolution of remote sensing images has gradually increased. my country's high-resolution remote sensing technology is developing rapidly. At present, a major special project of high-resolution earth observation system has been launched to improve the ability to obtain high-resolution remote sensing images and promote the progress of spatial information technology. The release of sub-meter-level resolution remote sensing images of Gaofen-2 satellite marks that my country's remote sensing earth observation has entered the sub-meter-level era, and its sub-satellite point spatial resolution can reach 0.8 meters. High spatial resolution remote sensing imag...

AI Technical Summary

Problems solved by technology

[0006] Aiming at the shortcomings of the existing high-resolution remote sensing image classification methods, the present invention proposes a high-resolution remote sensing image classification method based on a parallel hybrid convolution network, which combines the advantages of three-dimensional convolution and two-dimensional convolution to extract deep spatial Spectral features and fusion transformation to improve the classification accuracy and efficiency of high-resolution remote sensing images

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