Remote-sensing image building change detection method

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...

AI Technical Summary

Problems solved by technology

Methods based on traditional image processing are divided into three levels, pixel-level, feature-level, and target-level image processing. This type of method involves more manual work, resulting in many false change areas.

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