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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

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

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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