Research on landslide hazard information extraction method based on SAR image

3.1 SAR imaging principle

Synthetic Aperture Radar (SAR) is a new type of detection technology based on the development of radar technology. The radar emits electromagnetic waves, and the target is ranged and detected by electromagnetic wave scattering [5, 6]. The imaging principle of the SAR image is shown in Fig. 1.

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After the ground object receives the electromagnetic wave, the two-dimensional distribution of the backscattering exists in the plural form, thereby forming the SAR image data, that is, the real part and the imaginary part constitute each ground resolution unit, and the SAR image is the reconstructed ground target scattering. A two-dimensional deconvolution process of coefficients [7].

Before and after the landslide disaster, the gray information and texture information of the SAR image will change. According to the grayscale texture information of the landslide area, the grading texture information can be extracted by using the grayscale texture feature. The grayscale texture recognition method adopted in this paper mainly includes two steps: the first stage is texture feature extraction, the second stage is landslide information recognition [8, 9], and the flow chart is shown in Fig.

For mountainous areas with complex terrain, the landslide results obtained using only grayscale texture information are not accurate enough. Therefore, in this paper, the landslide results are obtained by using the grayscale texture information. The landslide information obtained from the result is suspected landslide information, and then the final landslide information is judged based on the results of the change information obtained from the two SAR images before and after the landslide disaster. It is considered that the area where both change and suspected landslide is the real landslide area [10].

The combined image is detected and analyzed, and the gradation of each pixel in the image is divided into a landslide region ω l and a non-landslide region ω ⁢ n . The probability distribution function of the pixel value of the composite image X is set to p ⁢ ( X ) , and the distribution function contains two types of conditional probability distributions, i.e.

p ( X ) = p ( X | ω l ) p ( ω l ) + p ( X | ω n ) p ( ω n )(1)

The superscripts t and t + 1 represent the current t and the next iteration of t + 1 . Similarly, these equations can also be used to estimate the mean and variance of the conditional probability density function of ω l and the prior probability. The estimation of the parameter is started from its initial value, and the final value is obtained by iterating the above formula to convergence. Under the assumption that the individual pixel values are independent of each other, the Bayes minimum error principle is such that the pixel X ⁢ ( i , j ) in the combined image X belongs to the category ω k , so that the posterior conditional probability needs to be maximized. That is written as:

ω k = arg max ω i ∈ { ω n , ω l } { P ( ω i | X ( i , j ) ) } = arg max { P ( ω i ) p ( X ( i , j ) | ω i ) }(2)

This is equivalent to solving the maximum likelihood boundary T 0 for the two types of ω n and ω l on the combined image. The optimal threshold T 0 is given by the following equation:

p ⁢ ( ω l ) p ⁢ ( ω n ) = p ( X | ω n ) p ( X | ω l )(3)

Under the condition of Gaussian probability density distribution, it can be obtained by Eq. (2)

( σ n 2 - σ l 2 ) ⁢ T 0 2 + 2 ⁢ ( μ n ⁢ σ l 2 - μ l ⁢ σ n 2 ) ⁢ T 0 + μ l 2 ⁢ σ n 2 - μ n 2 ⁢ σ t 2 - 2 ⁢ σ n 2 ⁢ σ t 2 ⁢ ln ⁡ [ σ c ⁢ p ⁢ ( ω n ) σ n ⁢ p ⁢ ( ω c ) ](4)

The optimum threshold T 0 is thus calculated. Here, μ l and σ l are used as the mean and standard deviation of the landslide training samples in the combined image. Similarly, μ n and σ n are the mean values of the non-landslide training samples in the combined images. Standard deviation. According to the formula, the equation for calculating the optimal threshold is a one-ary quadratic equation, so that two thresholds are obtained as T o ⁢ 1 and T o ⁢ 2 ( ), respectively. It is considered that the area where the gray value is between the thresholds T o ⁢ 1 and T o ⁢ 2 is the landslide area, otherwise it is the non-landslide area.

4.1 Test and results discussion

The gray-scale co-occurrence matrix is calculated by using the SAR image after the earthquake disaster in the area around Yingxiu Town of Sichuan Province. The eight feature images are calculated by the gray level co-occurrence matrix, and eight gray-scale texture information images are obtained. It is easy to find each gray from the image. Degree texture features have different recognition capabilities for different features.

According to the optimal threshold obtained by calculation, it is considered that the region where the gray value of the synthesized image pixel is between 2.7 and 3.2 is a landslide, otherwise it is a non-landslide region, and thus the obtained landslide information result is shown in Fig. 2.

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The phenomenon of mountain shadows, overlapping and other phenomena caused by SAR imaging is partially extracted as landslide information in the landslide information result map, and buildings in some areas are also misjudged as landslide information, which leads to certain extraction results.

In this paper, the method of combining the change detection information is used to extract the final landslide result to improve the accuracy of the result. It is considered that only the changed region can extract the landslide information through the gray texture information method. The resulting landslide information is shown in Fig. 3a.

Figure 3b shows an aerial image taken on May 26, 2008 after the Wenchuan earthquake, with a maximum resolution of 0.2 m. In Fig. 3b, the three areas A, B, and D are typical landslide areas, and their central latitude and longitude is (31 ∘ 02’22.59 ′′ N, 103 ∘ 29 ′ 07.29 ′′ E) (31 ∘ 03 ′ 00.09) ′′ N, 103 ∘ 29’ 13.49 ′′ E) (31 ∘ 03 ’44.59 ′′ N, 103 ∘ 29 ’41.11 ′′ E). The SAR image was taken on May 16, 2008 after the Wenchuan earthquake. Corresponding to the central latitude and longitude of the three regions A’, B’, and D’ in Fig. 3a is (31 ∘ 02’21.89 ′′ N, 103 ∘ . 30’08.51 ′′ E) (31 ∘ 03’00.23 ′′ N, 103 ∘ 19’10.56 ′′ E) (31 ∘ 03’45.21 ′′ N, 103 ∘ 29’50.11 ′′ E). This is because some of the gravel is broken after the collapse of the artificial building. The grayscale texture information reflected by the sediment and the sliding body in the surrounding mountains is similar to that of the landslide, and this area is greatly changed due to the earthquake, so the area is misjudged as the landslide area.

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The selected verification data is located in and around Maoxian County, Sichuan Province. The area is mainly dominated by bare soil and low vegetation, and the vegetation with higher growth is relatively small, and there are also Maoxian county towns and rivers flowing in the study area.

In order to ensure the accuracy of the verification results, while the Radarsat-2 satellite transited on August 28, 2017, our researchers made simultaneous measurements on the latitude and longitude coordinates of the landslide area. The Radarsat-2 image data is obtained as fully polarized data, and in order to be consistent with the study area image, HV polarized image data is used in the verification process. During the time when the two images were acquired, several landslides occurred in the area due to rainfall, causing some roads to be interrupted and the village to be destroyed. However, since the landslide disaster is caused by rainfall, the regional distribution of the landslide is relatively scattered. In some mountainous areas where landslides occur, many places cannot obtain latitude and longitude by manpower.