CN108776144B - Remote sensing identification method for ocean oil spill emulsion based on group spectral characteristics - Google Patents
Remote sensing identification method for ocean oil spill emulsion based on group spectral characteristics Download PDFInfo
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Abstract
The invention relates to a remote sensing identification method of ocean oil-spilling emulsion based on group spectral characteristics, which utilizes unique spectral characteristics and distribution modes thereof caused by oil-water emulsion groups (-CH, -OH) to realize the rapid, accurate and effective identification of different types of oil-water emulsions in hyperspectral images and the classification. Compared with the traditional observation means, the method can provide timely and efficient guidance information, does not need to waste a large amount of manpower and material resources, can accurately identify and classify different types of marine oil spilling emulsions only by using the hyperspectral remote sensing reflectivity images based on accurate radiation correction and atmospheric correction, and can effectively avoid misjudgment as the gulfweed (the gulfweed is similar to certain oil spilling emulsions in visual characteristics and spectral morphological characteristics, and examples are given in the description of the invention for explanation). The method can meet the requirement of monitoring the oil spilling and improve the working efficiency of oil spilling pollution treatment.
Description
Technical Field
The invention relates to the technical field of remote sensing ocean oil spill monitoring, in particular to a hyperspectral remote sensing identification and classification method for different types of ocean oil spill emulsions based on group spectral characteristics.
Background
According to incomplete estimation, 47% of marine oil spill events are caused by human activities and occur in various processes including oil exploitation, processing, transportation and the like, and the marine ecological environment is seriously harmed by the oil spill pollution caused by the human activities, so that huge economic losses are caused. In addition, under the comprehensive influence of factors such as wind, wave and flow, the oil spill pollution can cause large-scale and long-term harm to seawater, surface atmosphere, seabed, coastal zones and the like. For example, the oil spill accidents of deep sea oil wells in 2010 in the gulf of Mexico in the United states and the oil spill accidents in China and even in new harbors in 2010 cause huge environmental and economic losses.
In the event of a marine oil spill accident, the oil spill is mixed with water under the action of marine power, and a stable or unstable (water-in-oil phase or oil-in-water phase) oil-water mixture (oil-water emulsion) is formed. Wherein, the water-in-oil phase oil-spilling emulsion means that the seawater is dispersed in the continuous crude oil in the form of small droplets. Oil-in-water phase oil spill emulsions are continuous seawater in which dispersed droplets of crude oil are present. Once the water-in-oil emulsion is formed, the water-in-oil emulsion is more difficult to clean and recover, the existing surfactant, oil spill recovery equipment and the like cannot effectively play a role, and the damage to the marine environment is more obvious. The oil spill emulsion visually appears as a brown, orange or yellow "custard", "mousse" strip, which is very similar to marine gulfweed. In the form of a reflection spectrum, gulfweed is not the only marine organic matter capable of lifting the red-edge reflectivity, and the marine oil-spilling emulsion can also cause the lifting of the reflectivity of a near-infrared band but is not due to the red-edge effect; the similarity of oil emulsions to the visual and spectral characteristics of the order sargassum further increases the difficulty of identifying and classifying the two.
The hyperspectral remote sensing technology is a novel optical remote sensing frontier technology which is mature only in the later period of the 90 th century in the 20 th century, hyperspectral remote sensing images contain approximately continuous surface feature spectral information, and diagnostic spectral absorption characteristics can be detected through fine spectral resolution, so that subtle differences among surface features are reflected. The groups of different types of oil emulsions and the chlorophyll, cell walls, etc. of the gulfweed produce unique absorption characteristics in their respective spectral curves, which are presented in the form of peaks and valleys whose location and number provide sufficient information to detect and identify different targets. Therefore, the method can accurately and timely identify the oil spilling emulsions of different types and distinguish the oil spilling emulsions from the gulfweed, is applied to marine oil spilling pollution treatment work, and has important practical significance for monitoring marine oil spilling pollution.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the problems that the oil spilling pollution area is difficult to be monitored manually, the water color background noise is large, different oil spilling emulsions and gulfweed have the same visual characteristics, similar spectrum forms and are difficult to distinguish at present, the method combines hyperspectral remote sensing images, realizes the identification and classification (distinguishing) of the oil spilling emulsions and the gulfweed by detecting the characteristic spectra of respective unique groups of the oil spilling emulsions and the gulfweed, and has important significance for monitoring the marine oil spilling pollution.
In order to solve the technical problems, the remote sensing identification method of the marine oil spill emulsion based on the radical spectral characteristics, provided by the invention, comprises the following steps:
Preprocessing the hyperspectral remote sensing image data to obtain hyperspectral reflectivity data;
step 2, enhancing spectral information
Calculating the difference value of the background water body spectrum of the hyperspectral reflectivity data, and subtracting the background water body spectrum pixel by pixel to weaken the influence of the background water color; carrying out normalization processing on the hyperspectral reflectivity data to obtain a normalized reflectivity value;
step 3, detecting the characteristic spectrum of the radicals
The characteristic groups of different types of oil emulsions and gulfweed exist in different peak-valley point distribution modes in a high spectral curve. The peak-valley information of the hyperspectral curves is indicative spectral characteristics for distinguishing different substances, the same substances have similar peak-valley point distribution patterns, and the different substance peak-valley point distribution patterns are obviously different.
For each pixel, detecting the peak and the trough of the spectral curve of each pixel as group characteristic points (the position of the peak and trough points in the spectral curve can be extracted by utilizing twice differential calculation), wherein the group characteristic point data are stored as [ pos, pk, value ], pos is the spectral wavelength of the detected group characteristic points, pk is a peak and trough identifier, value is a normalized reflectance value, and the group characteristic point data of each pixel form the characteristic spectral data of the pixel;
Selecting a plurality of typical characteristic spectrum data of the oil-spilling emulsion as training samples, wherein the typical characteristic spectrum data of the oil-spilling emulsion refers to the characteristic spectrum data obtained by determining the spectrum of the oil-spilling emulsion through the steps 1-3, further utilizing peak-valley marks pk in [ pos, pk, value ], dividing each typical characteristic spectrum data into peak characteristic point spectrum data peak and trough characteristic point spectrum data rough,
calculating the similarity matching degree between every two spectral feature data by using the following formula:
＝|dF(peakf，peakg)-dF(troughf，troughg)|
in the formula (d)F(peakf，peakg) Peak characteristic point spectral data peak for two characteristic spectral datafAnd peakgThe discrete Frechet distance between, dF(troughf，troughg) Trough feature point data rough for two pieces of feature spectrum datafAnd troughgThe discrete Frechet distance between; the discrete Frechet distance considers the values and distribution modes of points on the curves, and can effectively calculate the similarity between the curves, and the calculation model of the discrete Frechet distance relates to parameters m, n and Q, wherein Q is max (m, n); calculating dF(peakf，peakg) Then, m and n are the number of peak characteristic points of the two characteristic spectrum curves respectively; calculating dF(troughf，troughg) Then, m and n are the number of the trough characteristic points of the two characteristic spectrum curves respectively;
the maximum value of the similarity matching degree obtained by calculation is used as a similarity matching degree threshold value0；
The similarity matching calculation method based on the discrete Frechet distance is different from a common similarity calculation method, and the numerical value and the distribution mode of points on a curve can be considered, so that the difference of the numerical value is calculated, and the form similarity between the curves is also considered; on the basis, the similarity matching degree calculation method based on the discrete Frechet distance also considers the difference between the peak point and the valley point (the reason for forming the peak and the valley of the reflection spectrum curve is different).
And calculating the similarity matching degree between the characteristic spectrum data of each pixel in the classified images to be recognized and the average spectrum data of the training samples, if the similarity matching degree is greater than the similarity matching degree threshold value, the pixel is a marine oil-spill emulsion, and merging the pixels which are judged to be the marine oil-spill emulsion to obtain a recognition result.
The invention has the following beneficial effects:
according to the invention, by means of fine characteristic spectrum information contained in the hyperspectral image, based on respective unique group characteristic spectrum of the oil emulsions with different types and different from the characteristic spectrum of the gulfweed, the oil emulsions with different types can be effectively identified, and the phenomenon of misjudgment caused by the same visual characteristic and similar spectrum form is eliminated. The practical effect shows that the method can reduce the monitoring time, labor and material cost of the oil spilling area, ensure the monitoring precision and improve the real-time monitoring efficiency.
The application example of the invention can further enhance the popularity and effectiveness of the hyperspectral remote sensing technology in the marine oil spill oil film pollution monitoring application, and can better serve the marine application industries of marine environment monitoring, marine oil spill pollution assessment, marine oil spill quantity estimation, oil spill pollution damage claim and the like.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
FIG. 2 is an image of different types of oil spill zones and gulfweed zones.
FIG. 3-a is the raw spectrum of Sargassum.
FIG. 3-b is a background water difference spectrum of Sargassum.
FIG. 3-c is a normalized spectrum of Sargassum.
FIG. 4-a is the original spectrum of an oil-in-water emulsion.
FIG. 4-b is a normalized spectrum of an oil-in-water emulsion.
FIG. 4-c shows the original spectrum of the water-in-oil emulsion.
FIG. 4-d is a normalized spectrum of a water-in-oil emulsion.
FIG. 5 is a diagram showing the results of the radical characteristic spectrum detection of the present invention.
FIG. 6 is a graph illustrating normalized similarity index for training-recognition according to the present invention.
FIG. 7 is a graph showing the results of the oil emulsion and Sargassum identification.
FIG. 8 is a graph showing the classification results of different types of oil emulsions according to the present invention.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention.
The embodiment is applied to 4-scene AVIRIS airborne hyperspectral image data, the spectral range is 350-2400nm, and the spectral resolution is 10 nm; areas 1-3 are different types of oil-spilling emulsion research areas, and area 4 is a gulfweed research area; when the method is implemented on the 4-scene hyperspectral image, the distinguishing and identifying effects of the inter-class (oil-spilling emulsion and gulfweed) and the intra-class (two types of oil-spilling emulsion) are accurate; according to the method, a distinguishing result graph is given to the gulfweed area so as to show that the gulfweed area is not mistakenly judged as other substances on the identification and classification of different oil spilling emulsions, and the effectiveness and the accuracy of the gulfweed area are shown; the method realizes the classification of different types of oil spilling emulsions and has good classification effect.
As shown in fig. 1, a flow diagram of a method for hyperspectral remote sensing identification and classification of different types of marine oil spill emulsions based on group spectral features according to an embodiment of the present invention includes the following steps:
The image preprocessing comprises radiometric calibration and atmospheric correction, and high spectral reflectivity data is obtained. On the basis, the water vapor wave band is removed, 5-point smoothing is carried out on the image element spectrum, and fine errors such as atmosphere are eliminated.
As shown in fig. 2, the true color composite image of the 4-scene AVIRIS research area is preprocessed in step 1.
And 2, enhancing spectral information.
The method comprises the following steps: and calculating the spectrum difference value of the background water body according to the high spectral reflectivity data, and subtracting the spectrum of the background water body pixel by pixel to weaken the influence of the color of the background water. As shown in FIG. 3-a, for Sargassum which is greatly influenced by water color background, the background water body difference is calculated to obtain a difference spectrum chart 3-b, and then normalization processing is performed to obtain a result shown in FIG. 3-c. The step can effectively eliminate the deviation of the calculated reflectivity value caused by the difference of the radiation energy, and increase the uniformity of the spectrum shape of the same substance and the difference of the spectrum shapes of different substances.
By using a lower partThe hyperspectral reflectivity data is normalized by the formula to obtain a normalized reflectivity value βi：
βi＝(αi-αmin)/(αmax-αmin)i＝1,2,3,…,N
Wherein N is the number of the pretreated wave bands, i is the wave band number in the hyperspectral data, αiReflectance value representing band i, αmaxAnd αminRespectively representing the maximum and minimum of reflectivity in one spectral curve.
As shown in fig. 4-a and 4-c, the raw spectra of two oil emulsions are shown, and fig. 4-b and 4-d are the corresponding spectra after the spectrum normalization process. Step 2 can concentrate the spectral characteristics of the same type of substances to be the same, and can effectively distinguish the spectral characteristics of different substances and increase the difference.
And 3, detecting the radical characteristic spectrum.
As shown in fig. 5, the radical characteristic spectrum detection result is a schematic diagram, and there is a significant difference between the radical characteristic spectra of the oil-spill emulsion and the gulfweed. This is illustrated in fig. 5. The positions and reflectance values of the groups contained in the oil emulsion and the gulfweed are different, and the following table shows the positions of the groups detected by the oil emulsion and the positions of the groups detected by the gulfweed.
TABLE 1 group position table detected by emulsified oil and Sargassum
The invention is realized by utilizing the difference of the reflectivity peak value or the reflectivity valley value of the emulsified oil and the groups contained in the gulfweed in different wave bands. The method comprises the following specific steps:
and detecting the peak and the trough of the spectral curve of each pixel as group characteristic points.
The peaks and troughs of the spectral curve are detected by the following method:
i＝i+4-i,i＝1,2,3,…,N-2
wherein i is the band number in the hyperspectral data, βiAnd βi+4Respectively represent the normalized reflectance values of the band i and the band i +1, ifiIf 2, the spectral curve of the pixel is a trough at the band i, if so, the spectral curve of the pixel is a troughiAnd 2, the spectral curve of the pixel is a peak at the wave band i.
The group feature point data is stored as [ pos, pk, value ], pos is the spectral wavelength of the detected group feature point, pk is the peak-valley identification, pk-2 represents the peak, and pk-2 represents the valley. value is a normalized reflectance value, and the group characteristic point data of each pixel constitutes the characteristic spectrum data of the pixel.
After the processing is completed, the [ pos, pk, value ] array is stored for each pixel of the AVRIS image.
And 4, calculating a similarity matching degree threshold value.
And selecting a plurality of typical characteristic spectrum data of the oil-spilling emulsion as training samples, wherein the typical characteristic spectrum data of the oil-spilling emulsion refers to characteristic spectrum data obtained by determining the spectrum of the oil-spilling emulsion through the steps 1-3, and further dividing each typical characteristic spectrum data into peak characteristic point spectrum data peak and trough characteristic point spectrum data trough by using peak-valley identification pk in [ pos, pk, value ].
Calculating similarity matching degree between every two spectral feature data by using the following formula, and taking the maximum value of the calculated similarity matching degree as a similarity matching degree threshold value0。
＝|dF(peakf，peakg)-dF(troughf，troughg)|
In the formula (d)F(peakf，peakg) Peak characteristic point spectral data peak for two characteristic spectral datafAnd peakgThe discrete Frechet distance between, dF(troughf，troughg) Valley features for two characteristic spectral dataData of points roughfAnd troughgThe discrete Frechet distance between;
the calculation formula for calculating the discrete Frechet distance of the two spectral data is as follows:
dF(f, g) is the discrete Frechet distance between spectral data f and g. The calculation model of the discrete frechet distance involves parameters m, n and Q, where Q is max (m, n); calculating dF(peakf，peakg) Then, m and n are the number of peak characteristic points of the two characteristic spectrum curves respectively; calculating dF(troughf，troughg) And m and n are the number of the trough characteristic points of the two characteristic spectrum curves respectively.
The discrete frechet distance model comprises a parameterization process (mathematical process). s is a parameter and belongs to [1, Q ], alpha and beta are a set of characteristic points of peaks (or troughs) pointing to two characteristic spectrum curves, and f (alpha) and g (beta) are respectively the values of the peaks (or troughs) corresponding to the alpha and the beta; α: [1, Q ] → [0, m ], β: [1, Q ] → [0, n ], simultaneously mapping α and β with s to express a synchronous process; for example, when s is 2, α and β each take a value of 3 and 4, they represent a 3 rd and a 4 th peak (or trough), respectively, and represent euclidean distances between the 3 rd feature point f (3) of the spectral data f and the 4 th feature point g (4) of the spectral data g. Mathematical models of the discrete frechet distance can be found in the paper: eiter T, Mannila H.computing Distance free Distance [ J ]. See Also,1994,64(3): 636-637.
The remote sensing spectral feature point data is combined with the existing discrete Frechet distance calculation formula, namely parameters m, n and Q are related to the spectral feature points, so that the discrete Frechet distance between the two spectral feature data is solved, and the similarity of the two spectral curves is expressed.
And 5, identifying and classifying the oil spilling emulsion.
And (4) identifying the entire scene AVIRIS by using the sample and the similarity threshold value in the step (4) and a similarity calculation method based on the discrete Frechet distance, applying a normalized similarity index to increase an identification target, and recording the pixels larger than the similarity threshold value as an identification result. And on the basis of the identification result, generating a classification result by combining the identification results.
The similarity matching degree is normalized by the following formula:
j' similarity matching degree, n, between spectral feature data of pixel j representing image to be recognized and average spectral data of training samplej' is a normalized index if nj' greater than n0And the pixel j is the ocean oil spill emulsion.
In this embodiment, the normalized similarity matching degree threshold n0＝0.7。
As shown in fig. 6, the normalized similarity index of 2 views of AVIRIS image is calculated in step 5 of the present invention, the value of each pixel is the possibility that the pixel is identified as a target (oil emulsion or gulfweed), and the similarity matching threshold value in step 5(s) (s) ()00.7) are plotted in the statistical histogram; the statistical histogram obviously shows that the method effectively makes the difference between the target object and the non-target object outstanding in identifying the oil-spilling emulsion and the gulfweed, and has good identification effect.
As shown in fig. 7, the 4-view AVIRIS image is a graph of the recognition result after the method of the present invention is implemented. Therefore, the target substances (oil-spilling emulsion and gulfweed) are effectively distinguished from the background and other ground substances, and the identification effect is better;
as shown in fig. 7, different types of oil emulsions are classified based on the identification method of the present invention; taking the two spectra (WO emulsion and OW emulsion) on the left side of fig. 8 as a training set, the two group differences detected in step 3 are shown in fig. 8 (c); based on the application, the WO emulsion and the OW emulsion are identified and then overlapped to obtain a classification result.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all modifications of equivalent structures and equivalent processes performed by the present specification and drawings, or directly or indirectly applied to other related technical fields, are included in the scope of the present invention.
Claims (7)
1. A remote sensing identification method of ocean oil spill emulsion based on group spectral characteristics comprises the following steps:
step 1, data preprocessing
Preprocessing the hyperspectral remote sensing image data to obtain hyperspectral reflectivity data;
step 2, enhancing spectral information
Calculating the difference value of the background water body spectrum of the hyperspectral reflectivity data, and subtracting the background water body spectrum pixel by pixel to weaken the influence of the background water color; carrying out normalization processing on the hyperspectral reflectivity data to obtain a normalized reflectivity value;
step 3, detecting the characteristic spectrum of the radicals
For each pixel, detecting the peak and the trough of a spectral curve of the pixel as group characteristic points, storing the group characteristic point data as [ pos, pk, value ], wherein pos is the spectral wavelength of the detected group characteristic points, pk is a peak-trough mark, value is a normalized reflectance value, and the group characteristic point data of each pixel form the characteristic spectral data of the pixel;
step 4, calculating a similarity matching degree threshold value
Selecting a plurality of typical characteristic spectrum data of the oil-spilling emulsion as training samples, wherein the typical characteristic spectrum data of the oil-spilling emulsion refers to the characteristic spectrum data obtained by determining the spectrum of the oil-spilling emulsion through the steps 1-3, further utilizing peak-valley marks pk in [ pos, pk, value ], dividing each typical characteristic spectrum data into peak characteristic point spectrum data peak and trough characteristic point spectrum data rough,
calculating the similarity matching degree between every two spectral feature data by using the following formula:
＝|dF(peakf，peakg)-dF(troughf，troughg)|
in the formula (d)F(peakf，peakg) Peak characteristic point spectral data peak for two characteristic spectral datafAnd peakgThe discrete Frechet distance between, dF(troughf，troughg) Trough feature point data rough for two pieces of feature spectrum datafAnd troughgThe discrete Frechet distance between; the calculation model of the discrete frechet distance involves parameters m, n and Q, where Q is max (m, n); calculating dF(peakf，peakg) Then, m and n are the number of peak characteristic points of the two characteristic spectrum curves respectively; calculating dF(troughf，troughg) Then, m and n are the number of the trough characteristic points of the two characteristic spectrum curves respectively;
the maximum value of the similarity matching degree obtained by calculation is used as a similarity matching degree threshold value0；
Step 5, identifying and classifying the oil-spilling emulsion
And calculating the similarity matching degree between the characteristic spectrum data of each pixel in the classified images to be recognized and the average spectrum data of the training samples, if the similarity matching degree is greater than the similarity matching degree threshold value, the pixel is a marine oil-spill emulsion, and merging the pixels which are judged to be the marine oil-spill emulsion to obtain a recognition result.
2. The remote sensing identification method of marine oil spill emulsion based on group spectral characteristics according to claim 1, characterized in that: in the step 1, the wave band range of the hyperspectral remote sensing image data comprises 400-2400nm, the spectral resolution is better than 20nm, and the preprocessing comprises geometric correction, radiometric calibration, atmospheric correction and smooth filtering.
3. The remote sensing identification method for marine oil spill emulsions based on group spectral characteristics as claimed in claim 1, wherein in step 2, the hyperspectral reflectivity data is normalized by adopting the following formula to obtain a normalized reflectivity value βi：
βi＝(αi-αmin)/(αmax-αmin)i＝1,2,3,…,N
Wherein N is the number of the pretreated wave bands, i is the wave band number in the hyperspectral data, αiReflectance value representing band i, αmaxAnd αminRespectively representing the maximum and minimum of reflectivity in one spectral curve.
4. The remote sensing identification method of marine oil spill emulsion based on group spectral characteristics, according to claim 3, characterized in that: the peaks and troughs of the spectral curve are detected by the following method:
i＝i+1-i,i＝1,2,3,…,N-2
wherein i is the band number in the hyperspectral data, βiAnd βi+1Respectively represent the normalized reflectance values of the band i and the band i +1, ifiIf 2, the spectral curve of the pixel is a trough at the band i, if so, the spectral curve of the pixel is a troughiAnd 2, indicating that the spectral curve of the pixel is a peak at the wave band i.
5. The remote sensing identification method of marine oil spill emulsion based on group spectral characteristics according to claim 1, characterized in that: in step 4, the spectrum of the training sample may be extracted from the spectrum library or from the image to be recognized, and if the training sample is extracted from the spectrum library, the training sample data needs to be resampled to make the band and the spectral resolution consistent with the image to be recognized.
6. The remote sensing identification method of marine oil spill emulsion based on group spectral characteristics according to claim 1, characterized in that: the peak-valley mark pk-2 represents a peak, and the peak-valley mark pk-2 represents a trough.
7. The remote sensing identification method of marine oil spill emulsion based on group spectral characteristics according to claim 1, characterized in that: respectively identifying the water-in-oil phase emulsion and the oil-in-water phase emulsion by using the steps 1 to 5, and then combining the identification results to obtain the identification result of the marine oil spill emulsion.
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| CN111624164B (en) * | 2020-03-19 | 2023-06-02 | 自然资源部第一海洋研究所 | Ocean spilled oil type hyperspectral identification method based on spectral standard deviation feature analysis |
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