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Multivariate statistical analysis of remote sensing image change detection methods

Multivariate statistical analysis of remote sensing image change detection methods

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Based on remote sensing image change detection is, from a different time for remote sensing image analysis and quantitatively determine the change characteristics of the surface and process technology. Not at the same time, access to satellite remote sensing image change detection, is to carry out the resources survey, environmental monitoring, basic geographic database updates, etc. The key technology of earth observation technology, has the urgent scientific application requirements, and a wide range of applications.

For example, how to extract the surface cover change information from remote sensing image, in recent years has become an important research topic in the field of remote sensing application, which is based on long phase, multichannel multiband or multipolar, etc.

of the remote sensing image change detection is a hot research topic. This paper mainly revolves around how to resolution from long phase of spaceborne multispectral remote sensing images and synthetic aperture radar images automatically extracting change information quickly and efficiently to study, the key problem at the same time the structure of the phase difference between the image and extract the key technology of the two aspects of change area. Paper first studied the multichannel remote sensing images from the long phase structure difference image problem.

Multichannel remote sensing images because of the influence of the correlation between channels, compared with the single channel image change detection is more difficult, need to effectively change of concentration distribution in each channel information, constructs the phase differences between the images at the same time, not for the analysis of the information of change.

Aiming at the difficulty of multichannel change information from the perspective of Multivariate statistical analysis, makes a deep research in the following three aspects: 1 in view of the correlation between channels is difficult to eliminate the influence of the problem, the introduction of canonical correlation analysis in Multivariate statistical analysis method, the two phases of multichannel remote sensing images as two groups of Multivariate random variables, using Multivariate Alteration Detection MAD transformation to multiple spectrum channel information or change information to all the differences of restructuring, assigned to a set of discrete variable, the result of the correlation between maximally eliminate channel on the negative impact of the change Detection preliminary solved the problem of the difference image structure.

2 in view of the MAD method is difficult to fully focus changes effectively the problem of information, puts forward to signaltonoise ratio to measure the distribution of the change information, the introduction of minimum noise ratio transform MNF, realizes the MAD results contained in the change information and noise maximum separation, solved the effective concentration change information and construct the difference image problem. 3 discusses the use of remote sensing images from different sensors, direct comparison like yuan grayscale characteristics change detection is feasible.

Proposed the MNF/MAD method is used to from multiple source multichannel remote sensing image structure difference image plan, in Landsat7 ETM + and SPOT5 simages based on HRG image change detection experiment results confirm the effectiveness of the scheme. Multiple experimental zone of the experimental results show that based on canonical correlation analysis of MNF/MAD multivariate change detection method, effectively from long phase of multichannel isolated change information in remote sensing image, and focus on the differences between the images in a small number of components, these components are usually able to show a clear physical meaning.

At the same time, this method does not agree with the measurement scale, measuring equipment gain change, not the advantages of the linear radiation distortion, and therefore claim to the radiation characteristic normalization is low, the difficulty of data preprocessing is reduced. Then, the paper studied the extracting change from difference image change detection area.

Multivariate statistical analysis of remote sensing image change detection methods

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