Remote sensing image scene classification method based on multi-branch convolutional neural network fusion

A convolutional neural network and remote sensing image technology, applied in the field of remote sensing image scene classification with multi-branch convolutional neural network fusion, can solve problems such as poor classification effect, achieve poor classification effect, improve classification effect, and improve detection effect of ability
CN110443143BActive Publication Date: 2020-12-18WUHAN UNIV OF SCI & TECH
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Patents(China)
Current Assignee / Owner
WUHAN UNIV OF SCI & TECH
Publication Date
2020-12-18

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Abstract

The invention discloses a remote sensing image scene classification method based on multi-branch convolutional neural network fusion. First, the scene data set is randomly divided into a training set and a test set in proportion; then the data set is preprocessed and data amplified; the processed The final data are obtained through the object detection network and the attention network to obtain the object mask map and the attention map; then the original image, the object mask map and the attention map training set are respectively input into a CNN network for fine-tuning, and the optimal classification model is obtained respectively. , and then the three sets of test sets are used as input to obtain the output of the Softmax layer through the optimal classification model, and finally the outputs of the three sets of Softmax layers are fused at the decision level to obtain the final prediction result. The invention can improve classification accuracy and classification effect.
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Description

technical field

[0001] The invention relates to the technical field of remote sensing image scene classification, in particular to a remote sensing image scene classification method based on multi-branch convolutional neural network fusion. Background technique

[0002] As an important branch of remote sensing image processing technology, remote sensing image scene classification task is of great significance in both military and civilian fields. Scene classification aims to automatically predict a semantic category for each scene image through a learned classifier. However, remote sensing image scenes have rich variations in different colors, viewpoints, poses, spatial resolutions, etc. and various mixed objects, and several image scenes of different categories may be similar to each other in many aspects. To be precise, remote sensing image scene classification still faces challenges due to the problems of intra-class diversity and inter-class similarity.

[0003] Tradit...

Claims

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