CN102855609B - Shallow underwater topography construction method integrating hyper-spectral data and sparse sonar data - Google Patents
Shallow underwater topography construction method integrating hyper-spectral data and sparse sonar data Download PDFInfo
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Abstract
The invention relates to a shallow underwater topography construction method integrating hyper-spectral data and sparse sonar data and belongs to the technical field of underwater topography reconnaissance. According to the shallow underwater topography construction method, dimensions of a hyper-spectral remote sensing image are reduced by aid of a clustering center of sonar data, area division is performed on a low dimensional remote sensing image after dimension reducing, and interpolation is performed on sonar data inside each of the areas to obtain the underwater topography. According to the shallow underwater topography construction method, the hyper-spectral remote sensing image and sparse sonar data are organically combined, in the whole process, and two kinds of data compensate to solve the problem of underwater topography construction well. The remote sensing image and the sonar data are provided with coordinate information after geometric correction, and a certain fuzzy corresponding relation exists between a grayscale of the remote sensing image and the water depth, so that water depth in each depth homogeneous area changes slightly in area-divided remote sensing image, and interpolation results of sonar data are authentic.
Description
Technical field
The present invention relates to a kind of shallow water underwater landform construction method, especially relate to the underwater topography construction method of a kind of integrated high-spectral data and sparse sonar data, belong to underwater topography prospecting technique field.
Background technology
Frequent interactional area, the extra large land of the regional conduct of coastal, Offshore, geological location is superior, all kinds of aboundresources, mankind's activity is violent.From resources development and utilization angle, Offshore is the strategic Development Base of hydrocarbon resources, food resource, shipping resource, with this end in view carry out the job facilities such as ground coast protection, port and harbour construction, mudflat aquaculture, energy development, waterway development, cable laying all the utmost point need the submarine topography geomorphologic map of various accurate, different scales.
At present, obtaining of underwater topography data mainly comprises boat-carrying measurement and remote-sensing inversion.Wherein traditional boat-carrying acoustic measurement takes sounding device and the combined mode of positioning equipment to carry out the measurement of underwater topography conventionally, and sounding device is mainly sonar.Sonar can be divided into three kinds of modes by its principle of work: single-point type, single beam and multi-beam.Multi-beam sonar is measured and has been realized underwater topography face covering mapping, but multi-beam sonar equipment price is too expensive, so underwater topography data acquisition still uses single beam, single-point type sonar in a large number.While using single beam, single-point type equipment to carry out bathymetric surveying, carry out depth measurement flight course planning according to depth measurement feature on water, form the depth measurement band net line of going forward side by side and measure, the modes such as recycling interpolation extrapolation are carried out data encryption, thereby obtain test block underwater topography product.Because the equipment acquired results such as sonic echo depth finder are section survey, cannot represent submarine topography, geomorphology information comprehensively, in addition the restriction of the condition such as environment, time and funds, measurement data overlay area, upgrades the frequency and can not meet the requirement of oceanographic survey large region normalization, real time implementation.
20 world end of the sixties, U.S.'s remote sensing sounding technology that begun one's study.Different spectrum, spatial resolution remotely-sensed data are on the increase in recent years, and shallow water depth of water inverse model is enriched constantly, can be divided into the forms such as theoretical explanation model, semiempirical model and statistical model according to the principle of its inverting institute foundation.
Theoretical explanation model taking visible ray in water body radiation transfer equation as basis, by reasonable assumption abbreviation model, be aided with the inner multiple optical parametric of water body of surveying gained and carry out depth of water inverting, the method has clear and definite physical significance, but water body internal optics difficult parameters, can not widespread use to obtain.
Complicated and the difficult difficult point obtaining for theoretical explanation model parameter, semiempirical model taking light in water attenuation principle as basis, the empirical parameter that adopts the models such as water body attenuation and analysis to return gained combines and realizes the remote-sensing inversion of the depth of water, can be divided into single band model and multiband model according to the remote sensing wave band number adopting.
Be no matter taking visible ray in water body radiation transfer equation as basic theoretical model, or the semiempirical model of the attenuation principle in water body based on light, because water body optical characteristics is very complicated, optical parametric observed reading is difficult to obtain, affect the many factors of depth of water inversion accuracy, therefore model inversion precision is not high.
Statistical model is widely used in depth of water inverting with its simple inverse model, higher inversion accuracy, but the method needs a certain amount of actual measurement water depth value, thereby has restricted to a certain extent the popularization of model.
Summary of the invention
The present invention wants technical solution problem to be: the shallow water underwater landform construction method of a kind of integrated high-spectral data and sparse sonar data is provided, in conjunction with high-spectral data, carries out accurately the structure of shallow water underwater landform with more sparse sonar data.
In order to solve above technical matters, the invention provides the shallow water underwater landform construction method of a kind of integrated high-spectral data and sparse sonar data, comprise the following steps:
Step 1, data are prepared---through the conversion of absolute radiation value, the rejecting of clutter wave band, image rotation, bad line reparation and striped removal, image reverse rotation, geometry correction and the disposal of gentle filter target in hyperspectral remotely sensed image, with the sparse sonar data of coordinate information and Water Depth Information, described sparse sonar data is corresponding with target in hyperspectral remotely sensed image coordinate range;
Step 2, find the cluster centre point of sonar data---the water depth value of sonar data is carried out to cluster, and obtain all kinds of central values, the approaching measurement point of water depth value and described central value is as all kinds of central points;
Step 3, the semi-supervised dimensionality reduction of high spectrum--process in the remote sensing image obtaining through step 1, the pixel identical with described center point coordinate is as the sample point of semi-supervised dimensionality reduction, utilize semi-supervised Laplce's dimension-reduction algorithm of considering space length to carry out dimensionality reduction to processing through step 1 remote sensing image obtaining, obtain low-dimensional remote sensing image;
The division of step 4, depth of water homogenous area---use multi-scale division algorithm to cut apart to low-dimensional remote sensing image, low-dimensional remote sensing image is divided into some depth of water homogenous area;
Step 5, to sonar data interpolation---at the inner information diffusion method that uses of each depth of water homogenous area, sonar data is carried out to interpolation, obtains underwater topography.
The present invention organically combines target in hyperspectral remotely sensed image and sparse sonar data, and in whole process, two kinds of data, as a kind of complementary, have solved the problem that underwater topography builds well.First by the cluster centre point of sonar data, target in hyperspectral remotely sensed image is carried out to dimensionality reduction, then the low-dimensional remote sensing image after dimensionality reduction is carried out to region division, finally at each intra-zone, sonar data is carried out to interpolation and obtain underwater topography.Remote sensing image and sonar data through geometry correction all possess coordinate information, and there is certain fuzzy corresponding relation in the gray scale of remote sensing image and the depth of water, therefore can think after partition in remote sensing image, in the each depth of water homogenous area depth of water conversion less, sonar data interpolation result is truer.The present invention has utilized this rule just cleverly, and two kinds of data are combined and realized high precision, the low cost structure of underwater topography.
For the sonar data of measuring with respect to multi-beam face type, the data low price that the present invention is used.With respect to traditional remote-sensing inversion model, the present invention is without obtaining water body optical parametric observed reading, and the factor that affects underwater topography structure precision is less, and the underwater topography precision of structure is higher.
Further, the present invention improves Laplce's dimension reduction method.Tradition Laplce dimension reduction method is considered spectrum intervals, has ignored the spatial character between remote sensing image picture element, and the present invention to improve spectrum intervals be spectrum intervals and the distance of combining of space length, specific as follows:
The semi-supervised dimensionality reduction of high spectrum of step 3 is Laplce's dimensionality reduction of considering locus, and the distance threshold of dimension-reduction algorithm adopts associating Gauss distance, and associating Gauss range formula is as follows:
wherein d
ijrepresent the associating distance of j pixel of i pixel to the,
represent j pixel spectrum intervals of i pixel to the,
represent the space length of j pixel of i pixel to the,
In formula,
represent the water depth value of i pixel of spectral information,
represent the water depth value of j pixel of spectral information,
represent the water depth value of i pixel of spectral information K-band,
represent the water depth value of j pixel of spectral information K-band, parameter σ
srepresent the control coefrficient of spectrum intervals;
the coordinate figure of i pixel of representation space information,
the coordinate figure of j pixel of representation space information,
the coordinate figure of i pixel of representation space information K-band,
j cell coordinate value of representation space information K-band, σ
lthe control coefrficient of representation space distance.
Consider that space length is tens times of left and right of spectrum intervals, in the improved dimension-reduction algorithm of the present invention, the control coefrficient σ of spectrum intervals
sspan be 1-2, the control coefrficient σ of space length
lspan be 85-105.
Further, in described step 2, by k-means algorithm, sonar data is carried out to cluster analysis, find optimum cluster pattern, obtain all kinds of central values.
Further, in described step 2, water depth value and described central value deviation ± 0.2m are using interior measurement point as all kinds of central points.
In order to reduce operand, in step 1 of the present invention, also to the following pre-service of described sparse sonar data process, sonar data is divided into regular shipping lines measurement point and pass point two parts, regular shipping lines measurement point is carried out to vacuate according to picture dot size, use average as picture dot central point water depth value, then two parts data are merged to the sparse sonar data obtaining after data compression.
Innovative point of the present invention is: (1) for traditional non-linearity manifold dimension-reduction algorithm taking vector space model as basis, be applied to remotely-sensed data acquired results distributes concentrated in low dimensional manifold space, easily cause the phenomenon of mixed point of classification, this method is introduced the semi-supervised thought and the spatial relationship that embed based on figure, a kind of semi-supervised Laplce's dimension-reduction algorithm of considering space length has been proposed, by regulating spectral space associating Gauss distance parameter, controlled sample point low dimensional manifold embeds, and then utilize semi-supervised Laplce's algorithm to obtain taking space of matrices model as basic dimensionality reduction result, experiment shows that this arithmetic result is compared with traditional Laplce's dimension-reduction algorithm, separability in low dimensional manifold embedded space of all categories increases, be beneficial to follow-up cutting apart and classification.(2) in the process of semi-supervised dimensionality reduction, use Kmeans method to carry out repeatedly cluster to sonar point data, obtain the sampled point of optimum cluster centre as semi-supervised dimensionality reduction, make result and Water Depth Information after dimensionality reduction have higher correlativity.(3) method of diffusion of information is applied in the interpolation of sparse sonar point data, the method by select suitable Fuzzy information diffusion model by diffusion of information entrained sparse sample in monitoring space, and solve the corresponding predicted value of future position by the theory of fuzzy set, when having broken away from common interpolating method and choosing data, build the step of search circle, interpolation of data sparse to observation station and that be placed with specific rule has good effect.(4) propose the new approaches that a kind of underwater topography builds, utilize sparse banded sonar data and face to cover high-spectral data, take the integrated strategy of multi-source data, learn from other's strong points to offset one's weaknesses, reach the object that improves underwater topography structure precision.
Brief description of the drawings
Below in conjunction with accompanying drawing, the present invention is further illustrated.
Fig. 1 is the target in hyperspectral remotely sensed image data of the embodiment of the present invention.
Fig. 2 is the sonar data of the embodiment of the present invention.
Fig. 3 is that the sonar data cluster centre that Fig. 2 obtains is put the image picture element in corresponding Fig. 1.
Fig. 4 is the result figure after the semi-supervised dimensionality reduction of Fig. 1.
Fig. 5 carries out the result figure after multi-scale division to Fig. 4.
Fig. 6 utilizes information diffusion method to carry out to Fig. 2 the underwater topographic map that interpolation obtains.
Embodiment
The present embodiment builds near the bay Tampa Bay underwater topography Gulfian, east coast of the united states, and the high-spectrum remote sensing data of employing is a scape of 2004, and these data are totally 242 wave bands, 30 meters of spatial resolutions, as shown in Figure 1; The sonar data adopting is Tampa Bay single-point type sonar data in 2004, and between the adjacent course line of data, at a distance of 500m, on course line, each sonar point is at a distance of 3.5m, as shown in Figure 2.
The shallow water underwater landform construction method of the integrated high-spectral data of this example and sparse sonar data, mainly comprises the following steps:
Step 1, data are prepared.
1) high-spectral data is carried out to pre-service.The present embodiment data used are through processes such as blotch removal, echo correction, background removal, radiation correcting, the recovery of bad pixel and picture quality detections.But in practical application, still exist bad pixel need to carry out further pre-service, to reach the object that improves image availability, concrete grammar is as follows:
1. absolute radiation value conversion.High-spectral data, because actual emanations value is less, is convenient storage, has been multiplied by certain coefficient on original value basis, so need here all VNIR wave bands divided by all wave bands of 40, SWIR divided by 80.
2. wave band is rejected.Reject without the wave band 1～7,58～7,255～242 of radiation calibration overlapping wave band 56～57,77～78 and be subject to the wave band 121～127,167～178 of steam image.
3. bad line reparation and striped are removed.Make bad line and striped carry out bad line reparation, striped removal in vertical state image rotation.Method (the Goodenough DG that adopts the people such as Goodenough to propose, DykA, NiemannKO, et al Processing hyper-ion and ali for forest classification[J] .IEEE Transactionson Geoscience and Remote Sensing, 2003,41 (6): 1321-1331.) carry out automatic detection and the reparation of bad line.Adopt " overall situation is removed striped " method (" pre-service of EO-1Hyperion high-spectral data ", " sensor information " the 6th phase in 2005, author: Tan Ping Xiang, Li Zengyuan, Chen Erxue, Pang Yong) of the propositions such as the bright perfume of Tan to carry out the reparation of striped.Then image being carried out to reverse rotation is virgin state.
4. geometry correction.In this example, basis is carried out geometric accurate correction through the TM remotely-sensed data with accurate geographic position information of registration to high-spectral data.
5. smothing filtering.The stochastic error causing for eliminating the random variation of image numeric data, adopts 5x5 medium filtering to eliminate high frequency noise, obtains more level and smooth spatial spectral radiation numerical value, in order to rear depth of water inverting.
2) sonar data is carried out to pre-service.
On sonar course line, packing density is 0.5m/point, the highdensity measurement of course-and-bearing improves limited to inversion accuracy, but greatly aggravated the complicacy of calculating and the expense of internal memory, the extraction of taking point strategy of feature resampling to carry out sonar data has reached the object that reduces computing time and space complexity.
Concrete grammar is as follows: sonar data is divided into regular shipping lines measurement point and pass point two parts, regular shipping lines measurement point is carried out to vacuate according to picture dot size, use average as picture dot central point water depth value, then two parts data are merged to the sparse sonar data obtaining after data compression.
The cluster centre point of step 2, searching sonar data.The water depth value of sonar data is carried out to cluster, and obtain all kinds of central values, the approaching measurement point of water depth value and described central value is as all kinds of central points.
Find sonar data cluster centre as follows as the concrete grammar of semi-supervised sample point:
1) progressively increase cluster centre point number, and change initial random some number of cluster, ask repeatedly error mean in cluster result class, choose the cluster centre number of AME minimum in class as actual cluster centre number.
2) in step 1) under given cluster centre, progressively increase initial random some number of cluster, until after cluster in the accumulative total class of each bunch square error obviously increase;
3) select step 2) in 2 points of error minimum in accumulative total class, and near these 2, constantly change cluster centre point number, with approach may the accumulative total class of minimum in square error, obtain optimum cluster centre point.
The process of cluster is as shown in table 1, in this example in the time that cluster centre number is 13, sample number in the 13rd classification is 0, therefore give up 13, finally choosing Optimal cluster centers number is 12, optimum initial random counting is 195, and corresponding cluster centre is as follows :-11.1602 ,-9.3621 ,-3.8004 ,-13.9071 ,-15.1869 ,-8.2899 ,-19.7460 ,-24.5605 ,-5.1402 ,-10.7514 ,-12.1251 ,-6.7161 ,-16.4174.
Table 1
Step 3, the semi-supervised dimensionality reduction of high spectrum.Process the remote sensing image obtaining through step 1, stacked with the sonar data cluster centre point in step 2, obtain with cluster centre and put crossing image picture element, corresponding image picture element as shown in Figure 3, carries out semi-supervised dimensionality reduction taking these pixel points as sample point.
Tradition Laplce dimension reduction method is considered spectrum intervals, ignore the spatial character between remote sensing image picture element, and the present embodiment to improve spectrum intervals be spectrum intervals and the distance of combining of space length, utilize semi-supervised Laplce's dimension-reduction algorithm of considering space length to carry out dimensionality reduction to processing through step 1 remote sensing image obtaining, obtain low-dimensional remote sensing image.
The semi-supervised dimensionality reduction of high spectrum of the present embodiment is Laplce's dimensionality reduction of considering locus, and the distance threshold of dimension-reduction algorithm adopts associating Gauss distance, and associating Gauss range formula is as follows:
wherein d
ijrepresent the associating distance of j pixel of i pixel to the,
represent j pixel spectrum intervals of i pixel to the,
represent the space length of j pixel of i pixel to the,
In formula,
represent the water depth value of i pixel of spectral information,
represent the water depth value of j pixel of spectral information,
represent the water depth value of i pixel of spectral information K-band,
represent the water depth value of j pixel of spectral information K-band, parameter σ
srepresent the control coefrficient of spectrum intervals;
the coordinate figure of i pixel of representation space information,
the coordinate figure of j pixel of representation space information,
the coordinate figure of i pixel of representation space information K-band,
j cell coordinate value of representation space information K-band, σ
lthe control coefrficient of representation space distance.
Each piecemeal that conveniently whole high-spectral data is divided into 100*100 size for processing is distinguished dimensionality reduction, and the dimension that the present embodiment is set after dimensionality reduction is 2 dimensions, and the K neighborhood quantity of Laplce's dimension-reduction algorithm is set as 12.K field quantity is that (acquiescence is made as 8 or 12 for a parameter of Laplce's dimension-reduction algorithm, here choose 12), Laplce's dimensionality reduction basic thought is to describe a stream shape with a undirected authorized graph, ensure that each authorized graph can also keep the structure before original dimensionality reduction after dimensionality reduction, because this authorized graph is a local syntople, so determine an adjacent region threshold, be K adjacent region threshold.Concrete grammar is as follows:
1) read in related data and comprise image data, spatial position data and sampling number certificate.
2) use non-supervisory Laplce's dimension-reduction algorithm to obtain sample point as semi-supervised sampled point.
3) consider that according to sample point locus is used semi-supervised Laplce's dimension-reduction algorithm to carry out dimensionality reduction to the high-spectral data after cutting apart, setting K neighborhood quantity is 12, and spectrum intervals coefficient value is 1, and space length coefficient value is 100.
1. first traversal is treated dimensionality reduction data set
look for the Neighbor Points of each point, structure neighbour figure.?
in, calculate each some X
iwith spectrum and space between all the other points combine Gauss's distance, searching is with respect to each some X
i12 nearest points of Euclidean distance be classified as the Neighbor Points of required point, if some X
iwith X
jadjacency, figure mid point X
iwith X
jbetween there is a undirected limit of having the right.
2. use heat kernel function structure weight matrix W.In above-mentioned neighbour figure, for selecting a weights W in each limit
i, j, structure weight matrix W.If some X
iwith X
jbe adjacency, the weights of establishing limit are W
i,j=exp (|| X
i-Y
j||
2/ t), otherwise establish W
i,j=0.
3. according to sample point
the low-dimensional corresponding with sample point embeds
laplacian matrix is carried out to piecemeal processing, and bring original Laplacian dimensionality reduction equation into
in, ask for the low-dimensional embedded coordinate for the treatment of dimensionality reduction data set
The dimensionality reduction result of each piecemeal is merged, and final merging view picture dimensionality reduction result out as shown in Figure 4.
The division of step 4, depth of water homogenous area.Use multi-scale division algorithm to cut apart to low-dimensional remote sensing image, low-dimensional remote sensing image is divided into some depth of water homogenous area.
The present embodiment uses multi-scale segmentation method, and (multi-scale segmentation method algorithm can be with reference to " a kind of OO multi-scale segmentation of remote sensing images method ", " Beijing Jiaotong University's journal: natural science edition " the 4th phase in 2007, author: Tan's thoroughfare continuous heavy rain, Liu Zhengjun, Shen Wei) result after dimensionality reduction is carried out to multi-scale division, constantly adjust the yardstick threshold value of cutting apart, final by constantly adjusting yardstick threshold value, finally choose 62 as optimum segmentation yardstick, and carrying out on this basis piecemeal merging, the segmentation result obtaining is as shown in Figure 5.
Step 5, to sonar data interpolation.At the inner information diffusion method that uses of each depth of water homogenous area, sonar data is carried out to interpolation, obtain underwater topography, as shown in Figure 6.
Information diffusion method can be with reference to " information diffusion method and application thereof in fuzzy message processing ", " Northwest University's journal (natural science edition) " 04 phase in 1992, author: Wang Jiading, Huang Chongfu.
First use each piecemeal in step 4 to cut apart sonar data point, sonar data in each piecemeal is used the method for diffusion of information to carry out interpolate value, then the result of each piecemeal diffusion of information is stitched together, obtains the underwater topography information of whole example region.
In addition to the implementation, the present invention can also have other embodiments.All employings are equal to the technical scheme of replacement or equivalent transformation formation, all drop on the protection domain of requirement of the present invention.
Claims (4)
1. a shallow water underwater landform construction method for integrated high-spectral data and sparse sonar data, comprises the following steps:
Step 1, data are prepared---through the conversion of absolute radiation value, the rejecting of clutter wave band, image rotation, bad line reparation and striped removal, image reverse rotation, geometry correction and the disposal of gentle filter target in hyperspectral remotely sensed image, with the sparse sonar data of coordinate information and Water Depth Information, described sparse sonar data is corresponding with target in hyperspectral remotely sensed image coordinate range;
Step 2, find the cluster centre point of sonar data---the water depth value of sparse sonar data is carried out to cluster, and obtain all kinds of central values, the approaching measurement point of water depth value and described central value is as all kinds of central points;
Step 3, the semi-supervised dimensionality reduction of high spectrum---process in the target in hyperspectral remotely sensed image obtaining through step 1, the pixel identical with described center point coordinate is as the sample point of semi-supervised dimensionality reduction, utilize semi-supervised Laplce's dimension-reduction algorithm of considering space length to carry out dimensionality reduction to processing through step 1 target in hyperspectral remotely sensed image obtaining, obtain low-dimensional target in hyperspectral remotely sensed image;
The division of step 4, depth of water homogenous area---use multi-scale division algorithm to cut apart to low-dimensional target in hyperspectral remotely sensed image, low-dimensional target in hyperspectral remotely sensed image is divided into some depth of water homogenous area;
Step 5, to sonar data interpolation---at the inner information diffusion method that uses of each depth of water homogenous area, sparse sonar data is carried out to interpolation, obtains underwater topography.
2. the shallow water underwater landform construction method of integrated high-spectral data according to claim 1 and sparse sonar data, it is characterized in that: in described step 2, by k-means algorithm, sparse sonar data is carried out to cluster analysis, find optimum cluster pattern, obtain all kinds of central values.
3. the shallow water underwater landform construction method of integrated high-spectral data according to claim 2 and sparse sonar data, is characterized in that: in described step 2, water depth value and described central value deviation ± 0.2m are using interior measurement point as all kinds of central points.
4. the shallow water underwater landform construction method of integrated high-spectral data according to claim 3 and sparse sonar data, it is characterized in that: in step 1, described sparse sonar data is through following pre-service, sparse sonar data is divided into regular shipping lines measurement point and pass point two parts, regular shipping lines measurement point is carried out to vacuate according to Pixel size, use average as pixel central point water depth value, then two parts data are merged to the sparse sonar data obtaining after data compression.
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| CN103344954B (en) * | 2013-07-08 | 2014-07-02 | 国家海洋局第二海洋研究所 | Submarine topography construction method based on multi-source water depth data fusion |
| CN109544691B (en) * | 2018-11-05 | 2021-07-06 | 国家海洋局第二海洋研究所 | MF (multi-frequency) method for automatically fusing multi-source heterogeneous water depth data to construct high-resolution DBM (database management system) |
| CN109459759B (en) * | 2018-11-13 | 2020-06-30 | 中国科学院合肥物质科学研究院 | Urban terrain three-dimensional reconstruction method based on quad-rotor unmanned aerial vehicle laser radar system |
| CN111174912B (en) * | 2020-01-03 | 2021-02-23 | 南京大学 | Snapshot type dispersion ambiguity-resolving hyperspectral imaging method |
| CN113139337B (en) * | 2021-04-14 | 2023-09-22 | 扬州大学 | Partition interpolation processing method and device for lake topography simulation |
| CN115235431B (en) * | 2022-05-19 | 2024-05-14 | 南京大学 | Shallow sea water depth inversion method and system based on spectrum layering |
| CN115564743B (en) * | 2022-10-18 | 2023-11-03 | 故宫博物院 | Method and device for extracting typical spectrum of paper and related equipment |
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