Remote-sensing image building change detection method

A technology of change detection and remote sensing images, which is applied in the field of remote sensing image processing, can solve the problems of manual participation, etc., and achieve the effects of improving detection accuracy, reducing manpower burden, and good detection performance

Pending Publication Date: 2020-01-17
BEIJING RES INST OF URANIUM GEOLOGY
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Problems solved by technology

Methods based on traditional image processing are divided into three levels, pixel-level, feature-level, and tar

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  • Remote-sensing image building change detection method
  • Remote-sensing image building change detection method
  • Remote-sensing image building change detection method

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Embodiment Construction

[0039] In order to better illustrate a method for detecting building changes in remote sensing images based on an attention mechanism and a feature pyramid network involved in the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and specific embodiments:

[0040] Step 1, read in the image and preprocess it.

[0041] First, select the high-resolution remote sensing image T of the same area in two phases 1 and T 2 , including three channels of R, G, and B. The specific selection principle: select data taken in different years, similar dates, and climate and meteorological conditions, with consistent spatial resolution, and use the same preprocessing method to eliminate geometric and radiation differences. non-significant change.

[0042] Secondly, using the RPC Orthorectification Workflow process tool in the ENVI software, combined with the known high-resolution satellite remote sensing image RPC p...

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Abstract

The invention belongs to the technical field of remote sensing image processing, and particularly relates to a remote sensing image building change detection method, which comprises the following operation steps: (1) reading an image and preprocessing the image; (2) making a sample data set; (3) constructing a network model based on an attention mechanism and a feature pyramid; (4) selecting a training sample to train the network model; (5) selecting a verification sample to verify the network model; and (6) classifying by using the trained model, and outputting a final change detection result. According to the method, a feature pyramid network is introduced, an attention mechanism is used in the multi-scale feature fusion process of all levels, and features are enhanced layer by layer tobe used for target detection of different scales; through application of deformable convolution and cavity convolution, the network obtains the feature expression capability of automatically adaptingto object deformation, the feature size is reserved while the receptive field is expanded, multi-scale information is obtained, the false alarm rate is effectively reduced, and the detection precisionis improved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and in particular relates to a method for detecting building changes in remote sensing images. Background technique [0002] Change detection is an important research direction in the field of remote sensing, and it has a wide range of applications in both civilian and military applications, such as military target monitoring, battlefield intelligence analysis, land inspection, disaster assessment, urban planning and other fields. Change detection is a technical means to detect and extract information about changes in an object or phenomenon by making multiple observations of the object or phenomenon. [0003] Remote sensing technology has the advantages of macro, real-time, high efficiency, and high resolution, and provides a new technical means for building change detection. High spatial resolution, high spectral resolution, high temporal resolution, multi-platform, mul...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/084G06V20/176G06N3/045G06F18/214G06F18/241
Inventor 田青林陈俊秦凯陈雪娇李瀚波余长发
Owner BEIJING RES INST OF URANIUM GEOLOGY
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