Method for detecting remote sensing image change based on non-parametric density estimation

Abstract

The invention discloses a method for detecting remote sensing image change based on non-parametric density estimation, which mainly solves the problem that the estimation to the statistic items which relevant to a change type and a non-change type in a differential chart in the prior art has error. The realizing process of the method is that inputting two remote sensing images with different time-phase, removing noise of each channel of each image, obtaining noise-removing images of the two time-phase, and constructing difference images through adopting the change time-vector method, gathering the difference images into change type and a non-change type through applying K-means clustering algorism, obtaining the initial sorting results, and estimating the statistic items relevant to the change type and the non-change type in differential images through adopting non-parameter density estimation, carrying out the self-adapting space restriction combining the variable weight markov random field model, and obtaining the final change detecting results. The experimentation shows that the invention can effectively keeps the structure information of the images, removes insulation noise, improves the change detection processing efficiency, and can be used for the fields of disaster surveillance, land utilization and agriculture 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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