A Change Detection Method in Remote Sensing Image Based on Nonparametric Density Estimation

Abstract

The invention discloses a remote sensing image change detection method based on non-parametric density estimation, which mainly solves the problem in the prior art that there is a deviation in the estimation of statistical items related to the change class and the non-change class in the difference image. The implementation process is as follows: input two remote sensing images of different time phases, and denoise each channel of each image to obtain the denoised images of the two time phases, and use the change vector analysis method to construct the difference image; apply K- The mean value clustering algorithm clusters the difference images into change classes and non-change classes to obtain the initial classification results, and uses non-parametric density estimation to estimate statistical items related to change classes and non-change classes in difference images; combined with variable weight Marko The random field model is used to perform adaptive spatial constraints to obtain the final change detection results. Experiments show that the invention can effectively maintain the structural information of the image, remove isolated noise, improve the efficiency of change detection and processing, and can be used in the fields of disaster monitoring, land utilization, and agricultural investigation.

Description

technical field [0001] The invention belongs to the technical field of digital image processing and relates to change detection of multi-temporal remote sensing images, in particular to a change detection of remote sensing images based on non-parametric density estimation. Background technique [0002] Change detection technology refers to identifying change information by analyzing two images obtained in the same area but at different times. With the increasingly advanced technology and means of remote sensing image acquisition and the massive accumulation of remote sensing image data, change detection technology is widely used in environmental monitoring, land use / cover, forest / vegetation change analysis, disaster monitoring, agricultural survey, urban change analysis, military reconnaissance and The application of strike effect evaluation and other aspects is more and more extensive. [0003] In the published literature, the unsupervised change detection technology is ma...

AI Technical Summary

Problems solved by technology

[0008] The above methods assume that the statistical items related to the change class and the non-change class in the difference image conform to specific models such as Gaussian mixture model, generalized Gaussian mixture model, etc., which require a complex parameter estimation process, and the accuracy of parameter estimation will affect the change. The results of the detection, but the statistical items of the difference image in practice do not necessarily conform to these specific models, which makes these methods biased in the estimation of the statistical items related to the change class and the non-change class in the difference image, which in turn affects the change detection accuracy.

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