Single-phase airborne hyperspectral remote sensing damage range detection method

Abstract

The present application relates to a kind of hyperspectral image processing and damage assessment method, specifically relates to a kind of single time phase camera-borne hyperspectral remote sensing damage range detection method, solve the existing spaceborne hyperspectral remote sensing image lower spatial resolution, single method false alarm rate is higher, it is difficult to realize the technical problem of the damage range detection of target or target after being damaged.First, the original data of target area aerial hyperspectral remote sensing image after being damaged and the reflectivity data of ground object spectrometer are obtained;Then pre-processing is carried out;Third, the Mahalanobis distance difference value and spectral angle of each pixel to target and background center are calculated, and ratio calculation is carried out;Finally, binary segmentation is carried out, and the damage range is obtained.The single time phase camera-borne hyperspectral remote sensing damage range detection method uses aerial hyperspectral remote sensing image and ground object spectrometer, extracts the damage target material spread range or damage range, which does not need to compare with the remote sensing image before being damaged to obtain the damage range by change detection, effectively improves the damage range detection effect.

Description

Technical Field

[0001] The present invention relates to a hyperspectral image processing and damage assessment method, and in particular to a single-time camera-borne hyperspectral remote sensing damage range detection method. Background Art

[0002] Remote sensing, as a non-contact, long-distance detection and data acquisition method, is widely used in fields such as land resources, agriculture and forestry, and material detection and identification. In terms of damage effect assessment, visual or remote sensing images are usually used; based on change detection technology, damage information of the target or object is detected by analyzing the differences between remote sensing images of the same area before and after the damage. Hyperspectral remote sensing has the characteristics of image-spectrum integration, integrating image information and spectral information. Its image information can reflect information such as the geometric shape of the detected object, and the spectral information can fully reflect the physical structure and chemical differences of the detected object, providing a more detailed description of the material of the detected object. After the target or object is damaged, due to the influence of explosions, shock waves, etc., the target material will be scattered to a certain range around the target, or the target will be affected by the impact and cause anomalies on the surface within a certain range. Therefore, the damage range can be detected by detecting the target material dispersion range or surface anomalies.

[0003] In practical applications, hyperspectral imaging has the following phenomena: different objects with the same spectrum, different spectra for the same object, mixed pixels, and low spectral signal-to-noise ratio. This has a negative impact on its application effect, which is reflected in algorithms such as material identification and anomaly detection, and usually has a high false alarm rate. In addition, since the hyperspectral imaging bands in the range of 400nm-2500nm are as high as hundreds of bands, the spatial resolution of satellite-borne hyperspectral remote sensing images is low, which is suitable for the study of larger-scale targets and makes it difficult to detect the target or the damage range after the target is damaged. Summary of the Invention

[0004] The purpose of the present invention is to solve the technical problems that existing satellite-borne hyperspectral remote sensing images have low spatial resolution, high false alarm rate of single methods, and difficulty in detecting the damage range after the target or object is damaged, and to provide a single-time camera-based hyperspectral remote sensing damage range detection method.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] A single-time camera-mounted hyperspectral remote sensing damage range detection method is characterized in that it includes the following steps:

[0007] 1] Obtaining the original data of airborne hyperspectral remote sensing images of the target area after damage;

[0008] 2] Preprocess the original data of airborne hyperspectral remote sensing images to obtain airborne hyperspectral remote sensing images and reflectance data of airborne hyperspectral remote sensing images;

[0009] 3] Use the ground object spectrometer to collect reflectance data of the target area after damage to obtain ground object spectrometer reflectance data;

[0010] 4] Preprocess the ground object spectrometer reflectance data to obtain smoothed ground object spectrometer reflectance data;

[0011] 5] Re-collect the smoothed ground object spectrometer reflectance data obtained in step 4] according to the number of bands, spectral range and spectral resolution of the airborne hyperspectral remote sensing image reflectance data to obtain the re-collected ground object spectrometer reflectance data;

[0012] 6) Calculate the cosine angle between the reflectance data of the airborne hyperspectral remote sensing image obtained in step 2) and the reflectance data of the resampled ground object spectrometer obtained in step 5) pixel by pixel to obtain the spectral angle of all pixels;

[0013] 7] Calculate the Mahalanobis distance difference between the pixels of all airborne hyperspectral remote sensing images preprocessed in step 2] and the target center and the background center pixel by pixel;

[0014] 8) Ratio the Mahalanobis distance difference obtained in step 7) with the corresponding spectral angle obtained in step 6) pixel by pixel to obtain the similarity of all pixels;

[0015] 9] Calculate the mean and variance of the similarity of all pixels and perform binary segmentation to obtain a binary image. The area in the binary image with pixel values ​​greater than the threshold is taken as the damage range to complete the single-time camera-borne hyperspectral remote sensing damage range detection.

[0016] Furthermore, step 1 is specifically as follows:

[0017] 1.1. Calibrate the airborne hyperspectral sensor;

[0018] 1.2. Use the calibrated airborne hyperspectral sensor to collect raw data from airborne hyperspectral remote sensing images of the damaged target area according to a standard data collection process; the standard data collection process includes planning waypoints, collecting whiteboard data, collecting standard reflectance gray cloth data, and executing the route.

[0019] Furthermore, step 2 is specifically as follows:

[0020] The raw data of airborne hyperspectral remote sensing images are preprocessed to obtain reflectance data of airborne hyperspectral remote sensing images and floating-point airborne hyperspectral remote sensing images; the preprocessing includes lens correction, reflectance extraction, atmospheric correction, geometric correction and image stitching; each pixel value of the airborne hyperspectral remote sensing image represents the reflectance of the ground object; each pixel value is between 0 and 1.0.

[0021] Furthermore, step 3 is specifically as follows:

[0022] 3.1. Calibration of ground object spectrometer;

[0023] 3.2. Perform dark current correction and optimization on the calibrated ground feature spectrometer, and use the ground feature spectrometer to collect whiteboard data;

[0024] 3.3. Use a ground object spectrometer to collect reflectance data of the target material in the target area after damage to obtain ground object spectrometer reflectance data.

[0025] Furthermore, in step 4], preprocessing the reflectance data of the ground object spectrometer includes format conversion, export, water vapor absorption and noise band removal, and smoothing of the reflectance data of the ground object spectrometer.

[0026] Furthermore, step 5 is specifically as follows:

[0027] 5.1. Extract pixel spectral curve from any pixel in the airborne hyperspectral remote sensing image reflectance data in step 2;

[0028] 5.2. The spectral range of the smoothed ground object spectrometer reflectance data in step 4 is cut to be consistent with the spectral range of the pixel spectral curve in step 5.1;

[0029] 5.3. According to the number of bands and spectral resolution of the pixel spectral curve described in step 5.1, re-sample the smoothed ground object spectrometer reflectance data intercepted in step 5.2 to obtain the re-sampled ground object spectrometer reflectance data.

[0030] Furthermore, the reflectance data of the airborne hyperspectral remote sensing image obtained in step 2] and the reflectance data of the ground object spectrometer after re-sampling obtained in step 5.3 are vectors of the same dimension.

[0031] Furthermore, step 7 is specifically as follows:

[0032] 7.1. Calculate the mean value M of all pixels in the airborne hyperspectral remote sensing image in step 2;

[0033] 7.2. Calculate the image covariance matrix τ of the airborne hyperspectral remote sensing image in step 2 -1 ;

[0034] 7.3. Calculate the Mahalanobis distance difference D(x) between the pixel of the entire airborne hyperspectral remote sensing image and the target center and the background center pixel by pixel using the following formula:

[0035]

[0036] Where d is the reflectance data of the ground object spectrometer after resampling, and x is the pixel value.

[0037] Further, step 9】 is specifically:

[0038] 9.1, calculate the mean value u of the similarity of all pixels obtained in step 8】;

[0039] 9.2, calculate the variance σ of the similarity of all pixels obtained in step 8】;

[0040] 9.3, binary segmentation is performed on the similarity of all pixels obtained in step 8】 using the following formula to obtain a binary image;

[0041]

[0042] In the formula, binaryimg represents the pixel value after segmentation, and p is the similarity;

[0043] 9.4, the region with a pixel value of 1 in the binary image is taken as the damage range, and the single-time camera-borne hyperspectral remote sensing damage range detection is completed.

[0044] Further, the atmospheric correction in step 2】 is completed by using a standard reflectivity gray cloth;

[0045] In step 3.3】 the reflectivity data of the target material after damage is the average of not less than 5 reflectivity data.

[0046] The beneficial effects of the present application are:

[0047] 1. The single-time camera-borne hyperspectral remote sensing damage range detection method of the present application uses airborne hyperspectral remote sensing images and ground object spectrometers to extract the damage target material spread range or damage range, which does not need to compare with the pre-damage remote sensing image to obtain the damage range by change detection, reduces the image registration, change detection and other processing processes, reduces the systematic processing error, and makes up for the problem of low spatial resolution of the existing satellite-borne hyperspectral remote sensing image.

[0048] 2. The single-time camera-borne hyperspectral remote sensing damage range detection method of the present application uses the ratio of the Mahalanobis distance difference value to the corresponding spectral angle to effectively reduce the false alarm rate of damage range recognition and effectively improve the damage range detection effect.

[0049] 3. The single-time camera-borne hyperspectral remote sensing damage range detection method of the present application can be applied to other fields such as target material spread range or abnormal range detection after being affected. BRIEF DESCRIPTION OF DRAWINGS

[0050] Figure 1 is a flowchart of an embodiment of the single-time camera-borne hyperspectral remote sensing damage range detection method of the present application;

[0051] Figure 2is a red brick material reflectivity data graph of buildings collected by a ground object spectrometer in an embodiment of a single time phase airborne hyperspectral remote sensing damage range detection method of the present application;

[0052] Figure 3 is a schematic diagram of red brick material reflectivity data in Figure 2 after removing noise bands;

[0053] Figure 4 is a schematic diagram of red brick material reflectivity data in Figure 3 after smoothing processing;

[0054] Figure 5 is a schematic diagram of red brick material reflectivity data collected by a ground object spectrometer in an embodiment of a single time phase airborne hyperspectral remote sensing damage range detection method of the present application;

[0055] Figure 6 is a schematic diagram of a damage range in an embodiment of a single time phase airborne hyperspectral remote sensing damage range detection method of the present application. DETAILED DESCRIPTION

[0056] As shown in Figure 1 , a single time phase airborne hyperspectral remote sensing damage range detection method comprises the following steps:

[0057] Step 1, obtaining an airborne hyperspectral remote sensing image:

[0058] 1.1, calibrating an airborne hyperspectral sensor in a laboratory;

[0059] 1.2, collecting original data of an airborne hyperspectral remote sensing image of a damaged target area by the calibrated airborne hyperspectral sensor according to a standard data collection process; the standard data collection process comprises planning a flight point, collecting whiteboard data, collecting standard reflectivity gray cloth data, and executing a flight route;

[0060] Step 2, preprocessing the original data of the airborne hyperspectral remote sensing image to obtain an airborne hyperspectral remote sensing image and reflectivity data of the airborne hyperspectral remote sensing image;

[0061] The original data of the airborne hyperspectral remote sensing image of the damaged target area is preprocessed, and the preprocessing includes lens correction, reflectivity extraction, atmospheric correction, geometric correction, and image stitching to obtain an airborne hyperspectral remote sensing image and reflectivity data of the airborne hyperspectral remote sensing image in a floating point type; wherein each pixel value of the airborne hyperspectral remote sensing image represents ground object reflectivity, and each pixel value is between 0 and 1.0.

[0062] Preferably, during pre-processing, atmospheric correction needs to be completed using gray cloth with standard reflectivity. If atmospheric correction is performed during the summer midday period when sunlight is strong, gray cloth with a reflectivity of no more than 50% should be used for atmospheric correction. The reflectivity data of the gray cloth used for atmospheric correction should be the average of more than 5 pieces.

[0063] Step 3: Obtain reflectance data from the ground object spectrometer:

[0064] 3.1. Calibrate the ground object spectrometer in the laboratory;

[0065] 3.2. Perform dark current correction and optimization on the calibrated ground feature spectrometer, and use the ground feature spectrometer to collect whiteboard data;

[0066] 3.3. Use the ground object spectrometer to collect reflectance data of the target material in the damaged target area. Collect 5 reflectance data and average them to obtain the ground object spectrometer reflectance data;

[0067] like Figure 2 As shown, the reflectance data of red brick materials used in buildings was collected using a ground feature spectrometer.

[0068] Step 4: pre-processing the ground object spectrometer reflectance data to obtain smoothed ground object spectrometer reflectance data;

[0069] Since water vapor in the atmosphere absorbs light near the 1400nm and 1800nm ​​bands, there is almost no signal at these wavelengths. Therefore, the data near these bands needs to be removed.

[0070] Preprocessing the ground object spectrometer reflectance data to obtain smoothed ground object spectrometer reflectance data; preprocessing the ground object spectrometer reflectance data includes format conversion, export, water vapor absorption and noise band removal, and smoothing of the ground object spectrometer reflectance data; Figure 3 For the general Figure 2 Schematic diagram of the data after removing the noise band from the red brick material reflectivity data. Figure 4 For the general Figure 3 Schematic diagram of the data after smoothing the red brick material reflectance data after removing the noise band;

[0071] Step 5: Resample the reflectance data of the ground object spectrometer to obtain the reflectance data of the ground object spectrometer after resample:

[0072] Generally, the number of bands, band ranges, and spectral resolutions of airborne hyperspectral sensors and ground object spectrometers are different. The number of bands of ground object spectrometers is much larger than that of airborne hyperspectral sensors. The reflectance data of ground object spectrometers are re-collected to the spectral range, number of bands, and spectral resolution level of airborne hyperspectral remote sensing images. Specifically:

[0073] 5.1, Extract the pixel spectral curve from any pixel in the airborne hyperspectral remote sensing image reflectance data in step 2;

[0074] 5.2, Cut the spectral range of the smoothed ground object spectrometer reflectance data in step 4 to be consistent with the spectral range of the pixel spectral curve in step 5.1;

[0075] 5.3, Resample the smoothed ground object spectrometer reflectance data after cutting in step 5.2 according to the number of bands and spectral resolution of the pixel spectral curve in step 5.1 to obtain the resampled ground object spectrometer reflectance data;

[0076] As shown in Figure 5 , the data obtained after resampling after smoothing the ground object spectrometer reflectance data obtained in step 4;

[0077] Step 6, Calculate the spectral angle:

[0078] Calculate the cosine angle between the reflectance data of the airborne hyperspectral remote sensing image obtained in step 2 and the resampled ground object spectrometer reflectance data obtained in step 5 pixel by pixel to obtain the spectral angle of all pixels; wherein the reflectance data of the airborne hyperspectral remote sensing image and the resampled ground object spectrometer reflectance data are vectors of the same dimension, so the calculated spectral angle is the cosine angle of the two vectors;

[0079] Step 7, Calculate the Mahalanobis distance:

[0080] In a real airborne hyperspectral remote sensing image, it is usually impossible to obtain the real statistical values of the target and the background, so the statistical values of the entire airborne hyperspectral remote sensing image obtained in step 2 are taken as the background statistical values, and the statistical values of the ground object spectrometer reflectance obtained in step 5.3 are taken as the target statistical values;

[0081] 7.1, Calculate the mean value M of the pixels of the airborne hyperspectral remote sensing image;

[0082] 7.2, Calculate the covariance matrix τ of the entire image of the airborne hyperspectral remote sensing image -1 ;

[0083] 7.3, Calculate the Mahalanobis distance difference D(x) between the target center and the background center for each pixel of the entire airborne hyperspectral remote sensing image by the following formula:

[0084]

[0085] In the formula, d is the resampled ground object spectrometer reflectance data, and x is the pixel value;

[0086] Step 8, Calculate the similarity:

[0087] In order to suppress the false alarm rate and enhance the true value of the detection, the ratio is used as the similarity, and each pixel corresponds to a spectral angle and Mahalanobis distance;

[0088] Ratio the Mahalanobis distance difference D(x) obtained in step 7 to the corresponding spectral angle obtained in step 6 for each pixel to obtain the similarity of all pixels. Each pixel represents a similarity value, and a higher similarity value indicates a higher probability of being within the damage range.

[0089] Step 9: Extract the damage range:

[0090] 9.1. Calculate the mean u of the similarities of all pixels obtained in step 8;

[0091] 9.2. Calculate the variance σ of the similarity of all pixels obtained in step 8;

[0092] 9.3. Use the following formula to perform binary segmentation on the similarity of all pixels obtained in step 8 to obtain a binary image;

[0093]

[0094] In the formula, binaryimg represents the pixel value after segmentation, piexl represents the similarity, and the similarity is used as the pixel value before segmentation;

[0095] 9.4. The area with pixel value 1 in the binary image is regarded as the damage range, such as Figure 6 As shown, Figure 6 The white area in the middle is the area with a pixel value of 1 after segmentation, indicating the scattered area of ​​red brick material after damage, that is, the target damage range of red brick material.

[0096] The research results show that the damage range detection method of the present invention using a single-time camera onboard hyperspectral remote sensing is effective in detecting the damage range and has a fast processing speed, under the premise of only using airborne hyperspectral remote sensing images after damage. It can be used to detect the range of building materials scattered after building damage, the range of surface damage after damage, etc.

Claims

1. A single-time camera-mounted hyperspectral remote sensing damage range detection method, characterized in that: The following steps are involved: 1] Obtaining the original data of airborne hyperspectral remote sensing images of the target area after damage; 2] Preprocess the original data of airborne hyperspectral remote sensing images to obtain airborne hyperspectral remote sensing images and reflectance data of airborne hyperspectral remote sensing images; 3] Use the ground object spectrometer to collect reflectance data of the target area after damage to obtain ground object spectrometer reflectance data; 4] Preprocess the ground object spectrometer reflectance data to obtain smoothed ground object spectrometer reflectance data; 5] Re-collect the smoothed ground object spectrometer reflectance data obtained in step 4] according to the number of bands, spectral range and spectral resolution of the airborne hyperspectral remote sensing image reflectance data to obtain the re-collected ground object spectrometer reflectance data; 6) Calculate the cosine angle between the reflectance data of the airborne hyperspectral remote sensing image obtained in step 2) and the reflectance data of the resampled ground object spectrometer obtained in step 5) pixel by pixel to obtain the spectral angle of all pixels; 7] Calculate the Mahalanobis distance difference between the pixels of all airborne hyperspectral remote sensing images preprocessed in step 2] and the target center and the background center pixel by pixel; 8) Ratio the Mahalanobis distance difference obtained in step 7) with the corresponding spectral angle obtained in step 6) pixel by pixel to obtain the similarity of all pixels; 9] Calculate the mean and variance of the similarity of all pixels and perform binary segmentation to obtain a binary image. The area in the binary image with pixel values ​​greater than the threshold is taken as the damage range to complete the single-time camera-borne hyperspectral remote sensing damage range detection.

2. The method for detecting damage range using a single-time camera-mounted hyperspectral remote sensing system according to claim 1, wherein: Step 1] Specifically: 1.

1. Calibrate the airborne hyperspectral sensor; 1.

2. Use the calibrated airborne hyperspectral sensor to collect raw data from airborne hyperspectral remote sensing images of the damaged target area according to a standard data collection process; the standard data collection process includes planning waypoints, collecting whiteboard data, collecting standard reflectance gray cloth data, and executing the route.

3. The method for detecting damage range using a single-time camera-mounted hyperspectral remote sensing system according to claim 1 or 2, wherein: Step 2] Specifically: The raw data of airborne hyperspectral remote sensing images are preprocessed to obtain reflectance data of airborne hyperspectral remote sensing images and floating-point airborne hyperspectral remote sensing images; the preprocessing includes lens correction, reflectance extraction, atmospheric correction, geometric correction and image stitching; each pixel value of the airborne hyperspectral remote sensing image represents the reflectance of the ground object; each pixel value is between 0 and 1.

0.

4. The method for detecting damage range using a single-time camera-mounted hyperspectral remote sensing system according to claim 3, wherein: Step 3] Specifically: 3.

1. Calibration of ground object spectrometer; 3.

2. Perform dark current correction and optimization on the calibrated ground feature spectrometer, and use the ground feature spectrometer to collect whiteboard data; 3.

3. Use a ground object spectrometer to collect reflectance data of the target material in the target area after damage to obtain ground object spectrometer reflectance data.

5. The method for detecting damage range using a single-time camera-mounted hyperspectral remote sensing system according to claim 4, characterized in that: In step 4, the preprocessing of the ground object spectrometer reflectance data includes format conversion, export, water vapor absorption and noise band removal, and smoothing of the ground object spectrometer reflectance data.

6. The method for detecting damage range using a single-time camera-mounted hyperspectral remote sensing system according to claim 5, wherein: Step 5] Specifically: 5.

1. Extract pixel spectral curve from any pixel in the airborne hyperspectral remote sensing image reflectance data in step 2; 5.

2. The spectral range of the smoothed ground object spectrometer reflectance data in step 4 is cut to be consistent with the spectral range of the pixel spectral curve in step 5.1; 5.

3. According to the number of bands and spectral resolution of the pixel spectral curve described in step 5.1, re-sample the smoothed ground object spectrometer reflectance data intercepted in step 5.2 to obtain the re-sampled ground object spectrometer reflectance data.

7. The method for detecting damage range using a single-time camera-mounted hyperspectral remote sensing system according to claim 6, characterized in that: The reflectance data of the airborne hyperspectral remote sensing image obtained in step 2 and the reflectance data of the ground object spectrometer after re-sampling obtained in step 5.3 are vectors of the same dimension.

8. The method for detecting damage range using a single-time camera-mounted hyperspectral remote sensing system according to claim 7, wherein: Step 7] Specifically: 7.

1. Calculate the mean value M of all pixels in the airborne hyperspectral remote sensing image in step 2; 7.

2. Calculate the image covariance matrix τ of the airborne hyperspectral remote sensing image in step 2 -1 ; 7.

3. Calculate the Mahalanobis distance difference D(x) between the pixel of the entire airborne hyperspectral remote sensing image and the target center and the background center pixel by pixel using the following formula: Where d is the reflectance data of the ground object spectrometer after resampling, and x is the pixel value.

9. The method for detecting damage range using a single-time camera-mounted hyperspectral remote sensing system according to claim 8, wherein: Step 9] Specifically: 9.

1. Calculate the mean value u of the similarity of all pixels obtained in step 8; 9.

2. Calculate the variance σ of the similarity of all pixels obtained in step 8; 9.

3. Use the following formula to perform binary segmentation on the similarity of all pixels obtained in step 8 to obtain a binary image; In the formula, binaryimg represents the pixel value after segmentation, and piexl represents the similarity; 9.

4. The area with a pixel value of 1 in the binary image is used as the damage range to complete the single-time camera-mounted hyperspectral remote sensing damage range detection.

10. The method for detecting damage range using a single-time camera-mounted hyperspectral remote sensing system according to claim 9, characterized in that: In step 2, atmospheric correction is done using a standard reflectance grey cloth; The reflectivity data of the target material after damage in step 3.3 is the average value of no less than 5 reflectivity data.

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