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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 fused by multi-branch convolutional neural network, can solve problems such as poor classification effect, achieve poor classification effect, improve detection ability, and improve classification effect of effect

Active Publication Date: 2019-11-12
WUHAN UNIV OF SCI & TECH
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Problems solved by technology

[0006] In view of this, the present invention provides a remote sensing image scene classification method based on multi-branch convolutional neural network fusion, to solve or at least partially solve the technical problem of poor classification effect existing in the prior art

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

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Embodiment 1

[0090] This embodiment provides a remote sensing image scene classification method fused with a multi-branch convolutional neural network, please refer to figure 1 , the method includes:

[0091] Step S1: Divide the scene data set into a training set and a test set according to a preset ratio.

[0092] Specifically, the scene dataset refers to an open source image scene dataset, which contains multiple categories, and each category includes multiple images. The preset ratio can be set according to needs, such as 1:9, 2:8, 3:7 and so on.

[0093] In the specific example, take the NWPU-RESISC45 scene image dataset as an example. This dataset includes 45 scene classes, each class contains 700 images, and the pixels are 256×256.

[0094] Step S2: Preprocessing the images in the scene dataset.

[0095] Specifically, preprocessing the images in the scene data set is to adjust the format and size of the images in the scene data set to a form that can be processed by the CNN networ...

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Abstract

The invention discloses a remote sensing image scene classification method based on multi-branch convolutional neural network fusion. The method comprises the following steps: firstly, randomly dividing a scene data set into a training set and a test set in proportion; performing preprocessing and data amplification on the data set; obtaining an object mask graph and an attention graph according to the processed data through an object detection network and an attention network respectively; respectively inputting the original image, the object mask image and the attention image training set into a CNN network for fine adjustment; obtaining three groups of test sets, respectively obtaining optimal classification models, respectively obtaining outputs of Softmax layers through the optimal classification models by taking the three groups of test sets as inputs, and finally obtaining a final prediction result by fusing the outputs of the three groups of Softmax layers through a decision-making level. The classification accuracy and the classification effect can be improved.

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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/13G06F18/241G06F18/253
Inventor 边小勇陈春芳张晓龙盛玉霞
Owner WUHAN UNIV OF SCI & TECH
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