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A Land Cover Classification Method for High Resolution Remote Sensing Images

A technology of land cover and remote sensing images, applied in the field of high-resolution remote sensing image recognition, which can solve the problem that CNN cannot complete tasks well

Active Publication Date: 2022-04-15
WUHAN UNIV
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  • Application Information

AI Technical Summary

Problems solved by technology

In addition, handcrafted CNNs may not be able to perform the task well due to insufficient experimentation or inexperience.

Method used

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  • A Land Cover Classification Method for High Resolution Remote Sensing Images
  • A Land Cover Classification Method for High Resolution Remote Sensing Images
  • A Land Cover Classification Method for High Resolution Remote Sensing Images

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Embodiment Construction

[0048] Below by embodiment, further illustrate outstanding feature and remarkable progress of the present invention, only in order to illustrate the present invention and in no way limit the present invention.

[0049] The embodiment of the present invention provides a method for land cover classification of high-resolution remote sensing images based on automatic search of depth architecture, and the implementation steps are as follows:

[0050] (1) Using the public data fusion competition track-a high-spatial-resolution remote sensing imagery land cover classification dataset (DFCTrack1), the DFCTrack1 training dataset contains 2783 WorldView-3 images with a size of 1024×1024 pixels. The dataset contains five land cover categories: ground, tall vegetation, building roofs, water, and bridges, and its visualization is shown in Figure 1.

[0051] 1.1. Since the public dataset already has labels, the training set TrainA and TrainB are directly divided by 1:1.

[0052] 1.2. Use ...

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Abstract

The invention relates to a land cover classification method for high-resolution remote sensing images based on automatic search of deep architecture, which is used to automatically search for a convolutional neural network architecture suitable for a specific data set. This invention combines deep learning theory to design a hierarchical search space and cascade training strategy, transform the convolutional neural network design into a data-driven model, and constructs a series of lightweight operations as candidates to ensure the accuracy of the search network architecture. efficiency. This framework adapts the network architecture through the atrous pyramid pooling module to make it suitable for high-resolution remote sensing image recognition tasks. The present invention can solve the existing problems of manual design architecture expertise and high time cost requirements. It can automatically search for a suitable deep learning model for a specific high-resolution remote sensing image land cover classification data set, which can effectively improve model design efficiency and accuracy.

Description

technical field [0001] The invention belongs to the field of high-resolution remote sensing image recognition, in particular to a method for land cover classification of high-resolution remote sensing images based on automatic search of a depth framework. Background technique [0002] With the rapid development of remote sensing technology, a large number of high-resolution remote sensing images can now be provided. Compared with low-resolution images, high-resolution remote sensing images contain more detailed spatial information, which not only brings opportunities, but also poses challenges to the classification of remote sensing images. Classification and analysis based on high-resolution remote sensing imagery technology has been applied to land cover classification tasks. [0003] The task of land cover classification has long been a daunting task in remote sensing. Conventional methods rely only on low-level spectral and spatial features, such as histograms of orien...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06V20/10G06V10/764G06V10/82G06K9/62G06N3/04G06N3/08G06T3/40
CPCG06N3/084G06T3/4053G06T2207/10032G06T2207/30181G06T2207/20081G06T2207/20084G06V20/13G06N3/045G06F18/241
Inventor 钟燕飞王俊珏马爱龙郑卓
Owner WUHAN UNIV
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