Method and system for remote sensing image change detection based on conditional random field

Abstract

The invention discloses a remote sensing image change detection method and system based on a conditional random field. The method includes: calculating the change vector magnitude of the remote sensing image; using the FCM algorithm to obtain the unary energy item of the CRF according to the change vector magnitude; The binary energy item of CRF is obtained from the neighborhood, spectral angle information and change vector magnitude of each node; the change detection result is obtained by minimizing the energy of CRF through the cyclic belief propagation algorithm. The selection of the regularization parameter in the CRF energy function in the detection process is selected through the pseudo training sample set, and the corresponding final change detection result is obtained under the optimal regularization parameter. The invention can make the result of change detection more reliable and robust, and improves the accuracy of change detection.

Description

technical field [0001] The invention belongs to the technical field of image processing, and in particular relates to a remote sensing image change detection method and system based on a conditional random field. Background technique [0002] Change detection of remote sensing images is to quantitatively analyze and determine the characteristics and process of surface changes from remote sensing data of different periods. Scholars from various countries have proposed many effective detection algorithms from different angles and applied research. Generally speaking, according to whether training samples are needed in the detection process, change detection can be divided into three categories: unsupervised change detection algorithms, semi-supervised change detection algorithms Detection algorithms and supervised change detection algorithms. Because unsupervised change detection algorithms do not require training samples, and the modeling process does not require prior knowl...

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Problems solved by technology

[0003] On December 16, 2015, the Chinese patent database disclosed a SAR image change detection method based on non-stationary analysis and conditional random field (patent number: 201510526592.5), but the detection method is supervised change detection, which consumes a lot of time in practical applications Labor costs, etc. to construct training samples

Also disclosed in the prior art is a CRF-based unsupervised change detection method [Guo Cao, Xuesong Li & Licun Zhou. Unsupervised change detection in high spatial resolution remote sensing images based on a conditional randomfield model. European Journal of Remote Sensing, 2016, 49 :225-237.], this detection method is applied in the process of multi-spectral and multi-temporal remote sensing image detection, which can improve the detection accuracy, but in the CRF energy function of this method, the regularization parameter is obtained by trial and error, which is not robust Rod

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