Abstract
Abstract. Aiming at the significant loss of high frequency information during reducing noise and the pixel independence in change detection of multi-scale remote sensing image, an unsupervised algorithm is proposed based on the combination between Dual-tree Complex Wavelet Transform (DT-CWT) and Markov random Field (MRF) model. This method first performs multi-scale decomposition for the difference image by the DT-CWT and extracts the change characteristics in high-frequency regions by using a MRF-based segmentation algorithm. Then our method estimates the final maximum a posterior (MAP) according to the segmentation algorithm of iterative condition model (ICM) based on fuzzy c-means(FCM) after reconstructing the high-frequency and low-frequency sub-bands of each layer respectively. Finally, the method fuses the above segmentation results of each layer by using the fusion rule proposed to obtain the mask of the final change detection result. The results of experiment prove that the method proposed is of a higher precision and of predominant robustness properties.
Highlights
With the continuous development of remote sensing data acquisition technologies and the update cycle of image data is getting shorter and shorter, change detection technology, an important application in remote sensing image processing and analysis, has successfully playing an indispensable role in land use and cover, forest and vegetation cover, change detection of towns and roads, natural disaster relief and governance and geographic database updating as well as many other fields[1]– [3].Generally, change detection techniques can be grouped into supervised and unsupervised types
In order to solve the problem in the methods described above, scholars developed a remote sensing image change detection method based on the combination between Discrete Wavelet Transform (DWT) and Markov random Field (MRF)[12]
On the basis of concluding the previous research, this paper proposed an unsupervised remote sensing image change detection method based on the combination of Dual-tree Complex Wavelet Transform (DT-CWT) and MRF, short for DTCWT-MRF-Bayes
Summary
With the continuous development of remote sensing data acquisition technologies and the update cycle of image data is getting shorter and shorter, change detection technology, an important application in remote sensing image processing and analysis, has successfully playing an indispensable role in land use and cover, forest and vegetation cover, change detection of towns and roads, natural disaster relief and governance and geographic database updating as well as many other fields[1]– [3]. In order to solve the problem in the methods described above, scholars developed a remote sensing image change detection method based on the combination between Discrete Wavelet Transform (DWT) and Markov random Field (MRF)[12] These methods take advantage of the frequency analysis capability of WT and takes spatial correlation of pixels into account, better overcome single pixel independence, false change caused by noise and registration errors and other factors. On the basis of concluding the previous research , this paper proposed an unsupervised remote sensing image change detection method based on the combination of DT-CWT and MRF, short for DTCWT-MRF-Bayes For one thing, it takes full use of the good properties of DT-CWT which maintain the good time-frequency analysis capability of traditional discrete wavelet transform(DWT), and has the characteristics of approximate shift invariance, good directional selectivity, limited data redundancy and perfect reconstruction[26].
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