Remote sensing image variation detecting method on basis of weighted Gabor wavelet characteristics and two-stage clusters

Abstract

The invention discloses a remote sensing image variation detecting method on the basis of weighted Gabor wavelet characteristics and two-stage clusters. The processed objects include optical remote sensing images and SAR (synthetic aperture radar) images and the remote sensing image variation detecting method includes (1) generating difference images according to remote sensing image types; (2) subjecting the difference images to Gabor wavelet transform; (3) extracting multiscale and multidirectional characteristics of the Gabor wavelet transform of the difference images; (4) designing the weighting coefficient and acquiring the weighted Gabor wavelet characteristics; (5) clustering the weight Gabor wavelet characteristics by means of the two-stage cluster strategy; (6) acquiring variation detecting results. By the remote sensing image variation detecting method, loss of marginal information is reduced, stronger, weak and slight varying areas can be detected at the same time, the total mistake pixel number is decreased, more detail information is reserved and variation results can be effectively extracted.

Description

technical field [0001] The invention belongs to the technical field of digital image processing, and mainly relates to the research direction of multi-temporal remote sensing image change detection. Specifically, it is a remote sensing image change detection method based on weighted Gabor wavelet features and two-level clustering. The processing object also includes multi-temporal optical remote sensing. images and Synthetic Aperture Radar (SAR) images. This method can be applied to many practical problems of earth observation, such as forest resources survey, land cover / land use dynamic monitoring, environmental disaster estimation, urban planning and layout, impact evaluation, especially natural disaster monitoring and evaluation. Background technique [0002] Remote sensing technology is a technology that detects and identifies targets by sensing electromagnetic waves, visible light, infrared rays, etc. reflected or radiated by targets from a long distance. features. Ac...

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Problems solved by technology

At the same time, in order to reduce the impact of noise, the applicant [Y.Q.Cheng, H.C.Li, T.Celik, and F.Zhang, "FRFT-based improved algorithm of unsupervised change detection in SAR images via PCA and K-means clustering", in Proceeding of IEEE International Geoscience and Remote Sensing Symposium, Melbourne, Australia, July2013:1952-1955.] and Y.G.Zheng et al [Y.G.Zheng, X.R.Zhang, B.Hou, and G.C.Liu, "Using combined difference image and K-means clustering for SAR image change detection , "IEEEGeoscience and Remote Sensing Letters, vol.11, no.3, pp.691-695, 2014] using low-order fractional Fourier transform (Fractional Fourier Transform, FRFT) and PPB (Probabilistic Patch-Based) filters respectively Generate a noise-suppressed difference image, and then perform spatial clustering change detection to obtain satisfactory detection results, but still do not fully consider the local structure information of the difference image

In addition, with the improvement of the resolution of remote sensing images, the separability between changed and unchanged classes decreases, and the uncertainty and ambiguity increase, so that the existing change detection methods cannot effectively detect the change information of high-resolution remote sensing images. With a high probability of false detection and missed detection, it has become a bottleneck restricting the application of high-resolution remote sensing change detection, and a better change detection method is urgently needed

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